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Infant Death Rates Are Higher in Rural America – but Not for All Causes

Details the results of a new report from the National Center for Health Statistics on the five leading causes of infant death and how these mortality rates vary in rural and urban areas. Although rural infant deaths have been higher for some time, this is the first breakdown by causes, and reveals that for the most recent data, rural rates were only higher for three of these five causes.

Department Press Briefings : Department Press Briefing – February 15, 2018

Heather Nauert

Spokesperson

Department Press Briefing

Washington, DC

February 15, 2018


Index for Today’s Briefing

  • DEPARTMENT
  • COUNTERTERRORISM
  • ISRAEL/PALESTINIANS
  • TURKEY
  • AFGHANISTAN
  • PAKISTAN
  • TURKEY/RUSSIA/REGION
  • VENEZUELA
  • CUBA
  • IRAQ/REGION
  • IRAN
  • NORTH KOREA
  • MIDDLE EAST PEACE
  • INDIA/REGION

    TRANSCRIPT:


    2:53 p.m. EST

    MS NAUERT: Hi. Good afternoon, everybody. I am not attempting to bribe you with chocolate, but I did bring you some of the chocolate so that we wouldn’t eat all of them in our office. So please, have at it. Valentine’s Day, hope you all had a good Valentine’s Day.

    QUESTION: Happy Valentine’s Day.

    MS NAUERT: Thank you. Thank you. Good to see you. Okay, starting out with a few announcements first. And the other day I was so proud of your interest in the Diplomacy Center here at the State Department, in particular the jazz display that we have out. I walked through there the other day at the Diplomacy Center and saw these wonderful pictures of these jazz musicians who had played in some very interesting places. Dizzy Gillespie – I saw a picture of him, I believe it was in Havana. So that was fantastic. Thanks for your questions about that to our expert, and I’d encourage you to head on over there and check that out.

    But as you know, our expert, our historian was here to talk about Black History Month, African American History Month, and I have an announcement related to that. Tomorrow the State Department is honored to host the 9th Annual Historical Black Colleges and Universities Foreign Policy Conference. It’s organized by the Bureau of Public Affairs, which is the one I work for. The goal of the conference is to prepare the next generation of foreign policy leaders from historically black colleges and universities and also predominantly black institutions.

    As a part of this, we’re looking forward to discussing U.S. foreign policy priorities and also department career opportunities. This year we have 351 students and faculty coming to the State Department from all around the country. We look forward to hosting them. We’re focusing on nontraditional paths to foreign affairs, including STEM and other majors you’d not typically expect foreign affairs careers to come from.

    Several highlights of the conference include remarks by our new Assistant Secretary of Public Affairs Michelle Guida. I look forward to introducing you all to Michelle at some point in the near future. We have some panel discussions, and also a keynote address from Ambassador Ruth Davis.

    We’re proud that many different parts of the State Department are involved in the conference, including the U.S. Diplomacy Center, the Bureau of Human Resources, the Office of Recruitment, the Bureau of Educational and Cultural Affairs, our President – our Presidential Management Fellows, PMF. You’re a PMF, right? Yes. My new colleague, Leigh, who just joined us from Burma is a PMF.

    Also USAID will participate, and we’re looking forward to seeing all of the young people as future diplomats tomorrow.

    I’d also like to mention we have a couple guests in the back. So welcome to our new faces who are joining us today.

    QUESTION: (Off-mike.)

    MS NAUERT: You’re all the way in the back today. (Laughter.) Nice to see you. Hi, Nazira.

    Lastly, I’d like to announce that our Deputy Secretary John Sullivan is travelling to Europe. Today, the Deputy Secretary will depart on a trip to Europe that will take him to Munich, Rome, Kyiv, Riga, and also Brussels. He’ll begin the trip in Munich where he’ll lead the State Department delegation to the Munich Security Conference. At the conference, he’ll participate in a nuclear security and arms control panel discussion. He will also reaffirm the United States commitment to global nuclear security. While in Munich, he’ll also hold bilateral meetings with officials from Germany and other countries.

    He will then travel to Rome where he’ll meet with senior Italian officials to discuss cooperation on priorities in Ukraine, in Libya, the fight to defeat ISIS, the Sahel, and human rights and religious freedoms around the world. He will also deliver remarks at the American Studies Center on U.S.-Italian relationship and our cooperation on security issues.

    The next stop will be in Kyiv where he’ll meet with President Poroshenko, the prime minister, and also the foreign minister. He’ll stress the importance of Ukraine quickly implementing credible economic and anticorruption reforms and underscore U.S. support for Ukraine’s sovereignty and also territorial integrity.

    From there, Deputy Secretary Sullivan will travel to Riga to meet with senior Latvian officials land reaffirm NATO’s Article 5 commitment.

    His last stop will be in Brussels to lead the U.S. delegation to the G5 Sahel Donor’s Conference where he will discuss continued support for development, security, and political issues in the Sahel. So with that, I’d be happy to take your questions.

    QUESTION: Thanks, Heather, and thank you for the chocolates. Let’s start in the Gulf.

    MS NAUERT: Okay.

    QUESTION: Senator Corker has written Secretary Tillerson saying he’s lifting the blanket hold that he put on military sales to the GCC countries to try to get them to resolve their dispute, basically saying, well, this clearly didn’t help because they are still at a standstill. So we might as well start selling them a bunch of weapons again. Does the Secretary agree with the assessment that that standoff is kind of locked in place and that there hasn’t really been a lot of progress in resolving it?

    MS NAUERT: I think we first started talking about this in May or June of last year.

    QUESTION: Right.

    MS NAUERT: I mean, that has been such a tremendously long period of time for this dispute to have gone on. Senator Corker sent in his letter. I took a quick glance at it earlier today, so I can confirm that we did in fact receive that letter from Senator Corker. I think Secretary Tillerson, a while ago, said, look, we’re here to help. We have said to all the parties in the dispute that we’re happy to assist you in any way, but at some point you all have to sit down together and work out your differences. We can’t do it for you. So I think the Secretary just backed off and let those countries take the lead. I can’t speak for Senator Corker and why he sent his letter and exactly what was contained within that letter because, again, I just took a quick glance at it. But I think those countries have to be willing to resolve all of this themselves. And hopefully they eventually will because, as you can see, it starts to take – and many of our national security advisors and others have said this as well, that it can start to take a toll or an effect on the fight against ISIS.

    QUESTION: Well, that’s actually exactly what I was going to ask you about next —

    MS NAUERT: Yeah.

    QUESTION: — which is that, at the time, Tillerson and others said in the immediate sense this doesn’t have an impact, we’re still using Al Udeid, et cetera. But that if this was a – became a prolonged thing, that it could actually impede the fight against the Islamic State group. So given that this has been going on as long as it has, as you pointed out, are we at that point where it is actually impeding the fight against ISIS?

    MS NAUERT: Yeah, I would have to refer you to the Department of Defense because, as you talk about Al Udeid, that’s the base there that hosts many of our flights and our personnel who are fighting in the battle against ISIS in Iraq and in Syria. So I’d have to refer you to them on that. I’m not sure exactly where this stands right now and how it’s impacting it, but I know that our folks were very, very concerned about that.

    Okay, all right.

    QUESTION: Can we move on?

    MS NAUERT: Shall we move on to something else?

    QUESTION: Yes. Can we move on?

    MS NAUERT: Hi, Said. Yeah, sure.

    QUESTION: Thank you. Heather, I just wanted to follow up on a couple of questions that I asked on Tuesday.

    MS NAUERT: Okay.

    QUESTION: One, regarding the Israeli law that basically puts the Israeli academic institutions in the settlements under civil law, which is creeping annexation as per Israel’s terminology. Do you have any comment on that?

    MS NAUERT: Yeah, just a little bit of information. It’s not too incredibly new from what you’ve heard before, so let me answer your question before I get to your next one. Just hold on.

    I think the President has been clear on his views regarding settlements that settlement activity, especially unrestrained settlement activity, does not advance the cause for peace. My understanding is that some of these settlements are governed under Israeli military law, but I think the President has been clear in his position on this issue, and Israel has also come back to the United States and said to the President we will take your views into consideration before we engage in this.

    QUESTION: All right, but that is extending civil law. That is – in effect, that is really annexation, which is like a prelude to annexing.

    MS NAUERT: I’m not going to – I’m not going to be able to characterize it in the way that you are, but I think we’ve been clear about our position on settlements.

    QUESTION: Okay, great. Let me just follow up on something that you also said. You said “unrestrained.” Is that – does that mean that you are okay with somewhat restrained settlement activity?

    MS NAUERT: Look, our – we’ve had a lot of conversations with the Israeli Government. We’ve had a lot of conversations with Palestinians. Obviously, you know this is such a sensitive matter. With regard to matters that are so sensitive, we like to tread lightly. We don’t want to cause damage to the prospects for peace; we want the parties to be able to work it out. I will go back to saying I think the President has made his position on this clear. Jason Greenblatt, Jared Kushner have spent a lot of time in the region; our folks have as well. I believe David Satterfield was just in Israel just last week talking about some of these issues. So let me just let the diplomats and the experts work those things out.

    QUESTION: I promise, two more —

    MS NAUERT: Okay.

    QUESTION: — if would indulge me.

    MS NAUERT: Okay.

    QUESTION: Okay. The first one is that Palestinian President – Palestinian Authority President Mahmoud Abbas is coming to the United Nations next Tuesday. Is there anything that you want him not to say or say before the international community, because he is going to call on the international community to be involved, and not just the United States?

    MS NAUERT: I think if anything, and I’m not going to speak for him, but I know that we want to sit down and have some talks about peace between the Israelis and Palestinians, recognizing that it will be difficult for both parties to come to some sort of consensus and agreement. And ultimately, whatever is agreed to has to be agreed to by both parties, because they have to be willing to work on it and concede. We would love to see him sit down and say let’s start some peace talks. That would be optimal. Are we going to get that? I don’t know. But that’s as far as I’m going to go on that.

    QUESTION: And lastly —

    MS NAUERT: Yeah.

    QUESTION: — I promise. Lastly, today Congress placed Hamas – recommended that Hamas be brought – condemned by the international body and so on because it has used Palestinians as human shields. Now, to the best of my recollection, no Palestinian, no organization, no NGO, has ever complained that Hamas was using Palestinian as human shields, except the Israeli Army, which was attacking them basically to give cover to attacking civilians —

    MS NAUERT: Said, I’m afraid I don’t have any information —

    QUESTION: — I mean, do you have any —

    MS NAUERT: I don’t have any information on what you’re referencing right now about this comment that was made by a member of Congress, so I’m just not going to – I’m not going to go there.

    Okay, anything else on that? Okay.

    QUESTION: Well, I just wanted to jump back to the first question, actually.

    MS NAUERT: Yeah.

    QUESTION: Josh was asking about Qatar. It reminded me of something. I’ve just checked it. Secretary Tillerson actually spoke about this at the launch of the Qatar-U.S. Dialogue two weeks ago. I think it’s two weeks, three weeks ago. And he was in the presence of Secretary Mattis when he said it. He said, “As the Gulf dispute nears the eight-month mark, the United States remains as concerned today as we were at its outset. The dispute has had direct negative consequences, economically and militarily, for those involved as well as the United States.” And he said, “The united GCC would bolster our effectiveness countering terrorism and defeating ISIS.” So he kind of answered Josh’s question.

    MS NAUERT: Mm-hmm. Well, he did.

    QUESTION: This dispute does hurt the fight against ISIS.

    MS NAUERT: Okay. Unfortunately, I didn’t have the Secretary’s quote on that issue right in front of me. Dave, do you want to take Robert’s job? (Laughter.) We could use you right now. No – love you, Robert. But Dave, thank you for clarifying that. So I think the Secretary spoke to that. There you go. All right, thanks.

    Laurie, hi.

    QUESTION: Yes, I’d like to ask you about Turkey since the Secretary is there today.

    MS NAUERT: Yes.

    QUESTION: I just —

    MS NAUERT: Which – let me add on that issue, the Secretary and President Erdogan just finished up their meeting a short while ago. I don’t have the details about that meeting. That was a one-on-one meeting. Last I had heard is they were going into it. So we’re working on getting all of you a readout, and I’ll get you details on that just as soon as I can. Robert may have a few while we’re out here talking.

    QUESTION: Okay. Well, I —

    MS NAUERT: And he’ll alert me if we have anything.

    QUESTION: I’d like to write a – ask about a piece that Aykan Erdemir, a former Turkish parliamentarian, had yesterday in The Hill. And he said you need more leverage, more sticks in your dealings with Ankara, and suggested that you consider a range of sanctions if you can’t reach some agreement now. Are you considering any sanctions against Turkey? Because there are many issues that you have in dispute.

    MS NAUERT: Yeah, we certainly do have issues in dispute. As you know, the Secretary is on the ground there, so I’m not going to detail too much about what we may or may not do. But sanctions are always on the table with regard to different nations and areas that we may have difficulties with, but you also know we don’t preview sanctions.

    QUESTION: Okay. And if I could ask another, a second question. Is Turkey threatening to deny you, the U.S., access to any bases such as Incirlik?

    MS NAUERT: Not that I am aware of. We are operating there. Turkey is a part of the D-ISIS Coalition, in addition a NATO ally, so we continue our operations out of there. I’m not aware of any disruptions or any threats regarding disruptions.

    Okay, shall we move on? Okay, Nazira, hi.

    QUESTION: Hi, Heather. As you know, Afghanistan situation Mr. Atta Noor, the former governor in Mazar-e Sharif, has a problem since like month ago or more than that. Still he said to the unity government not able to solve the problem between him and unity government, and he is – he always announced for 15 provinces in Afghanistan to do the demonstration. Do you think this demonstration, if happen, doesn’t make destabilize all Afghanistan?

    MS NAUERT: I’m not aware of this demonstration that you’re mentioning. I think we would probably regard that as an internal matter, but let me take a – take a look at it and get back with you with some sort of a more fulsome answer. Okay?

    QUESTION: Thank you.

    MS NAUERT: Okay.

    QUESTION: A follow-up? Afghanistan?

    MS NAUERT: Yeah, sure. Hi, how are you?

    QUESTION: Hi. What do you expect from the Kabul reform process next week? Ambassador Wells was in Kabul this week. What other talks were held there? Do you have —

    MS NAUERT: Sure. So let me start out with our Principal Deputy Assistant Secretary Alice Wells. She was just in Afghanistan, so I’ll provide you a little bit of a readout of that trip, because one of the things that she’s doing is previewing the Kabul process, which is coming up in a couple weeks.

    Our PDAS, as we call it, for South and Central Asian Affairs Alice Wells traveled to Kabul this week to meet with Afghan partners in advance of the February 28th Kabul Process conference. She met with several top Afghan officials, including President Ghani, Chief Executive Abdullah, National Security Advisor Atmar, as well as with prominent Afghan political, business, and media representatives, to highlight the longstanding U.S.-Afghan partnership.

    Ambassador Wells also met with Resolute Support and U.S. Forces Afghanistan Commander General Nicholson and U.S. Special Representative for the Secretary General for Afghanistan and head of the UN Assistance Mission in Afghanistan Tadamichi Yamamoto to discussion ongoing U.S. and international community support for efforts to bring peace and security to Afghanistan and the region.

    And now – and then we have the Kabul process coming up. I’m not able to give you any details about who from the U.S. Government may or may not be joining. We just don’t have that kind of information yet. But I can tell you that we look forward to at least – Robert, I don’t know if it’s we’re observing or if we will be participating, but nevertheless we look forward to that process because we see it as a way to reiterate the U.S. commitment toward the Kabul process to bring together so many of our international partners who are going to have some candid discussions, we believe, on a range of issues, from peace to development to humanitarian aid and other issues. So we’ll look forward to that, and as we get closer I’ll try to bring you some more information.

    Okay?

    QUESTION: Something else in the region?

    MS NAUERT: Okay, something else in the region. Hi, go right ahead. Yeah.

    QUESTION: Thank you. Jahanzaib from Ary News Pakistan.

    MS NAUERT: Yeah.

    QUESTION: For the last couple of days there is a news in the town that United States has put forward a motion to place Pakistan on a global terrorist financing watchlist with an anti-money-laundering monitoring group, the FATF. Is that true?

    MS NAUERT: So what you’re talking about is – it’s called the FATF. There’s a plenary session that’s planned for that. This is basically the international community has this sort of longstanding, well, concern when it comes to the Government of Pakistan about what we consider to be deficiencies in the implementation of anti-money laundering, counterterrorism, and other types of issues similar to that.

    What this group does is it promotes better measures to crack down on counterterrorism or to work against terrorism and also money laundering as well. Some of those deliberations, I can’t confirm what took place because those are considered to be private.

    QUESTION: Pakistan introduced a new bill, kind of an ordinance, that all individuals or organizations designated as a terrorist by the United Nations will be considered as a terrorist in Pakistan too, and Pakistan are going to take stern actions against them. So it looks like Pakistan now start implementing on the U.S. strategy for South Asia.

    MS NAUERT: Okay.

    QUESTION: Do you have any comment on that?

    MS NAUERT: I don’t have any information on that. I’ll see if some of our experts from our SCA Bureau can get you more on that, okay? All right.

    Let’s move on. Hi, Cindy. How are you?

    QUESTION: Hi. Good, thank you. Sorry, going back to Turkey, if I may.

    MS NAUERT: Okay.

    QUESTION: The Turkish foreign minister has said that relations with the U.S. are at a critical point and has called for specific steps to restore trust. Would you characterize it that way?

    MS NAUERT: I don’t want to characterize it one way or another because – for a few reasons. One, the Secretary is on the ground there. The Secretary is doing what he does best, and that’s diplomacy, talking with other countries, talking with his counterparts. And so we have a productive series of meetings going on in Turkey. We have other individuals who are involved as well. Secretary Mattis, for example, is meeting with some of his Turkish counterparts, I believe yesterday and also today, but taking place elsewhere.

    Certainly, we have a lot to work on. I mean, there’s no doubt about that. There are certainly some tensions there. But we have a lot of areas where we can agree to work together. An example of that would be our – we have bases in Turkey. That is an issue or a matter that continues obviously to this day, as we were talking about a few minutes ago. They’re a member of the D-ISIS Coalition, and so that’s important. But I’m not going to get ahead of some of the Secretary’s meetings.

    Okay.

    QUESTION: Can we turn to Iran?

    MS NAUERT: Okay. All right.

    QUESTION: Does the U.S. —

    QUESTION: Libya?

    QUESTION: Does the U.S. see eye-to-eye with Turkey on Syria?

    MS NAUERT: There’s a what?

    QUESTION: Does the United States see eye-to-eye with Turkey on what they are doing in Syria?

    MS NAUERT: Well, we have said to Turkey – and we’ve talked about this a lot here so I don’t want to go over —

    QUESTION: But you —

    MS NAUERT: — an old road once again. But we’re encouraging everyone to stick to the fight against ISIS. We understand Turkey’s legitimate security concerns. Of course, we do. And the Secretary has been very clear about that, as has Secretary Mattis. We understand those security views. We value our NATO ally and respect those views about security; but let’s keep the eye on the ball, and that is ISIS.

    Hey, Abbie.

    QUESTION: I know that you probably don’t have this necessarily yet because you don’t have a readout from the meeting, but was one of the subjects the Secretary intended to talk about with President Erdogan the purchase of Russian weapon systems which were going against the CAATSA legislation?

    MS NAUERT: Right, so you’re talking about the proposed purchase of S-400s. I don’t think that that has been signed at this point. I don’t know exactly what the status is of that or if that would actually be a violation of CAATSA because it’s not happened yet. I can look into it and see if I can get something more.

    But we have long said to Turkey that, one, any kind of weapons purchases under NATO agreements have to be interoperable. My recollection is that S-400s are not considered to be anti – interoperable, basically meaning that other nations would be – in NATO would be able to work with that kind of equipment. My understanding is that they would not be operable. But in terms of CAATSA and all that, we’ll look into that.

    QUESTION: On Turkey?

    QUESTION: I have one follow-up on —

    MS NAUERT: Sure. Go ahead.

    QUESTION: — on the CAATSA legislation.

    MS NAUERT: Yeah.

    QUESTION: I know that January 29th was the first day that you were able to implement or enforce any of the sanctions that came forward from the CAATSA —

    MS NAUERT: Have you been gone since January 29th? (Laughter.)

    QUESTION: Maybe. And —

    MS NAUERT: Yeah? So we’re going back a ways. All right. Go right ahead.

    QUESTION: So I just wondered if since that day there had been any movement towards enforcing some of those sanctions against people who were still in violation and engaging with the Russian military or intelligence entities from the late October list.

    MS NAUERT: So let me step back to that first date, which was January the 29th. And the State Department piece of that was that that was the first day that we could begin to impose sanctions that would meet a certain threshold. It wasn’t just a financial threshold. It has a – there were a lot of factors that would weigh into this. So we didn’t have that – we didn’t have sanctionable activities on that first day. That was just the start date for which we could begin sanctioning companies and entities.

    We have hundreds of people around the world, not just here at the State Department in this building but at our missions elsewhere, whose job it is to comb through lots and lots of foreign transactions – sales, things of that sort – to determine if it meets that threshold of being above a certain level. Again, that’s not just financial. It takes a lot of things into consideration, and our sanctions people could perhaps get you some more information on that.

    So we continue to go through that. We continue to go through that process, and it’s just the beginning of that. Okay?

    QUESTION: Heather, can you see – are there – have there – have they determined that there are sales that are happening but have not met – that do not meet that threshold? In other words, sales with Russia’s defense and intelligence sectors that the U.S. is going to permit to proceed without us slapping them with CAATSA sanctions because it’s —

    MS NAUERT: Because maybe it would be too small? Is that the question? Are there some that —

    QUESTION: Well, there’s a criteria that a senior State Department official said was if it’s adverse to national security interests, which basically means you could take anything like, say, a multibillion-dollar sale to Turkey of the S-400 and say, “Well, we really don’t want to upset Turkey right now, so that’s okay.”

    MS NAUERT: Well, one of – remember, let me go back to one of the NATO agreements or pledges – perhaps I’m using the wrong word here, but – is that systems purchased by NATO members – pardon me – have to be interoperable. My understanding is that S-400s do not meet that standard.

    QUESTION: Right, but they don’t care and want to do it anyway.

    MS NAUERT: So because – because of that, we would oppose the purchase of that. But I don’t know what the status is of any proposed purchase. But in terms of your question, I mean, it would just be a hypothetical. I don’t know that we have a particular deal that we have spotted and said, “You know what? That doesn’t meet the threshold. Let’s let it pass.” This is all still fresh – a fresh piece of law, a fresh – that we started to be able to impose and implement and our people are still going through all this stuff.

    QUESTION: On Turkey?

    QUESTION: On Iran?

    MS NAUERT: So I would anticipate, though – and as you know, we don’t forecast sanctions, but I would be very surprised if there weren’t sanctions in the future. Okay?

    QUESTION: (Off-mike.)

    MS NAUERT: Okay. All right. Rich, hi.

    QUESTION: Venezuela?

    MS NAUERT: Sure.

    QUESTION: Okay. Thanks, Heather. Does the Secretary believe he has the support of regional allies for – and full support of regional allies for further sanctions against Venezuela? And how much does the environment within, the humanitarian crisis within Venezuela weigh into the U.S. and others’ decisions on how exactly to apply pressure on the regime there?

    MS NAUERT: Yeah. When we – first off to the humanitarian situation, I’m sure many of you, like I, have seen the horrific stories coming out of Venezuela. You’ve seen what families are having to choose to do – to leave their children in some instances in orphanages and on the streets because they don’t have the food or the necessary medical supplies to care for their own children. Sometime last year when I started this job, I remember reading a story about a child who was a few years old but just weighed 11 pounds, which is basically a heavy newborn weight.

    There is clearly a humanitarian situation that I would just phrase as a dire situation there. It is something that seems to be worsening. I know it has the attention of the region. You were just down there with Secretary Tillerson in the region in which that was one of the top conversations among many of the countries in Latin America. We discussed that issue in the Caribbean when the Secretary was in Jamaica, in Peru, in Argentina, and others. So they share our concerns. But when we impose sanctions and when other countries impose sanctions, the idea is to never have it harm the regular population. It’s to put the squeeze on the government so that the government will change its ways because we’ve seen this government does not seem to care about its people, but rather it cares about keeping itself in power.

    QUESTION: And for any further measures, is there anything left to ensure that you are targeting the Maduro regime as opposed to exacerbating this?

    MS NAUERT: Yeah. I’m not going to forecast sanctions, but that is something that our people, when they look at imposing sanctions and structuring sanctions activities, take a very close look at. I know we talk with various groups, NGOs, different government – departments of our government to try to make sure that these things are targeted – targeted. And we always talk about targeted sanctions, so it’s affecting the individuals or companies or entities and not the people themselves.

    However, let me say this, and this applies to North Korea as other places: Governments can decide how they choose to spend their money. And if they choose to – and some – and I’m not saying this about Venezuela, but if they choose to spend all their money on weapons – illegal ballistic missiles, a nuclear program – that is that government’s choice. It’s wrong, but that is that government’s choice. Again, that’s not Venezuela, that’s – I’m referring obviously to North Korea on that matter.

    I can tell you that I know there are many aid groups and other entities like that that are prepared to go in to provide humanitarian aid when that is needed and when we’re able to get in and provide that in Venezuela.

    QUESTION: Just finally, is there – does the administration feel any type of time pressure to try to get any further measures out ahead of the elections that the regime’s holding?

    MS NAUERT: I think we’re looking at all kinds of options. Okay?

    All right. Let’s move on.

    QUESTION: On Iran?

    MS NAUERT: Hey.

    QUESTION: Staying in the region?

    MS NAUERT: Yeah.

    QUESTION: A question on Cuba. The Journal of the American Medical Association released this report yesterday detailing the symptoms and experiences of 21 of the American personnel who have been affected by these health attacks, as you guys call them. Does the State Department support the release of this report? And do you find it consistent with your own internal investigations?

    MS NAUERT: Yeah. So let me start out by saying safety and security of Americans is always our top issue. That includes our own colleagues. We have seen the Journal of American Medical Association report that was put out just yesterday, JAMA as many people refer to it. Our embassy – and I want to make sure folks are aware of this – released a health alert. It is posted. I believe it’s on our U.S. embassy in Havana’s website. That basically alerts people to the fact that this JAMA report exists, this JAMA report exists, so that we could provide information not only to our personnel, but so that information can be provided to the general public who may still be choosing to travel to Cuba.

    So that’s the purpose of that. It was written by independent medical personnel who took part in evaluating and treating some of the injuries of our people. I won’t detail what came out in the report. You can take a look at it ourselves. But we’ve shared the link to the article in order to inform U.S. citizens about what the doctors believe may be some of the symptoms and medical reactions of some of those people who were affected.

    QUESTION: In the report it says that the doctors signed nondisclosure agreements in order to be able to obtain some of the information about these individuals. It also says, though, that the other doctors who evaluated as part of the peer evaluation before publication were unable to access some of that information. Is there —

    MS NAUERT: Let me answer your first question first. So you’re saying the doctors signed nondisclosure agreements?

    QUESTION: Right.

    MS NAUERT: Okay. If that is the case – and I don’t know that that is the case – let me remind you that there’s an investigation still ongoing. So if they were asked by the U.S. Government to sign that, I would think that would be a pretty good indication that we don’t want people talking about (a) the medical symptoms of individuals, (b) the names of individuals, because that is – that is their own information and they’re our employees. But in addition, this investigation is still ongoing, so it’s extremely important for us to not disrupt that investigation so we could figure out who’s responsible for this and what’s responsible for it.

    QUESTION: I think I know the answer to this question, then. But is there – are there things that you guys have determined in the investigation that were not allowed to be released in the report?

    MS NAUERT: I don’t have any information on that. I mean, that’s like so far in the weeds with our experts talking to the medical professionals in Pennsylvania. I just don’t have that level of detail.

    QUESTION: Sure. And one last question on this. At the time of the evaluations of the – of these personnel, 14 of them had still not been able to return to work because their symptoms were so severe. Can you give any information, any update on that, and whether or not they have?

    MS NAUERT: Yeah. I don’t have any – an update for you on that. I’ll see what I can get for you if that is, in fact, even releasable information. Okay?

    QUESTION: Okay.

    MS NAUERT: Okay.

    QUESTION: On Iraq? So – yeah.

    MS NAUERT: Okay, one more question on Iraq.

    QUESTION: Yeah. You said in the past that the U.S. is not contributing any money to the Iraqi reconstruction. Can you tell us what other ways U.S. is contributing to that reconstruction? And also —

    MS NAUERT: No, that’s not what I said. I talked about large-scale reconstruction projects. In the past, the U.S. Government, and many of you reporters who have been around for a bit remember this, that during the Bush administration and other administrations we would do these large-scale projects of building roads and building tunnels and bridges and all of that to provide that for communities in Iraq and Afghanistan. That’s not what we’re doing today. We are helping with reconstruction certainly, but this administration believes that a better approach to that, instead of spending trillions and trillions of dollars on that restructure or on that rebuilding, is to get other countries, the neighboring countries, involved as well.

    So the United States is certainly involved. The United States participated in the Kuwaiti conference where some countries ended up providing donations, loans, things of that nature, and we were certainly happy to have seen that happen. The United States’ priority though now is stabilization and providing some of the basics. But we think it’s a wonderful thing when other countries in the region will step up to the plate. The Saudis talked about this, for example. The Turks – I believe they gave – they offered up some money. But I recall the Saudis, I mean, saying you know what, we’re interested in this, we’re interested in helping out our neighbors and seeing what we can do about reconstruction. And there were more than 2,000 people or companies who were involved in that Kuwait conference.

    QUESTION: And speaking of neighbors, so today the deputy foreign minister of Iran said that Tehran will contribute to the efforts of the reconstruction.

    MS NAUERT: Yeah.

    QUESTION: How do you – do you have anything on that?

    MS NAUERT: Look, I know they have been. They have been trying to gain more of a toehold certainly in the region and in other places as well. I think we’ve been consistent in saying where Iran goes, trouble tends to follow. Where the regime goes, trouble tends to follow. I don’t mean the people themselves, but rather the regime. There can also be very strict regulations when you work with a nation like that. You may not be, as a country, getting all that you bargained for. It may be a lot more difficult and a lot more onerous than – than you think. So they’re certainly entitled to do that, but I would probably caution countries.

    QUESTION: Can I have —

    MS NAUERT: All right, Janne. Hi.

    QUESTION: Thank you very much. On North Korea. Recently, right after Secretary Tillerson’s South Korea trip, the Secretary said that the United States will make stronger sanctions against North Korea. What is the stronger sanctions and how does it different from existing sanctions?

    MS NAUERT: Yeah, so I hate to give you this answer again, because it’s the third time I’ve said it today. We’re not going to preview the sanctions. There is certainly more that we can do, there is certainly more sectors that we could look at sanctioning, and we continue our conversations with many other countries who may be looking at unilateral or multilateral sanctions.

    QUESTION: Heather.

    QUESTION: North Korea.

    QUESTION: (Off-mike)

    MS NAUERT: Hold on, hold on.

    QUESTION: When is the sanctions effect – I mean, new sanctions. When —

    MS NAUERT: When would they? I – Janne I can’t tell you that. I can’t tell you that. Okay?

    QUESTION: Okay, so it’s possible —

    MS NAUERT: If there are sanctions that are going to be announced, I will certainly let you know as soon as we are ready to announce those. Okay?

    QUESTION: North Korea?

    QUESTION: Heather, Vice President Pence, just before he arrived – I think he was in Tokyo – announced we would soon, I think he said within several days or a week or so, be unveiling the toughest sanctions U.S. has ever put on North Korea. So I don’t understand this policy that the U.S. doesn’t preview what sanctions to – I mean, it seems like in some cases, the White House certainly does.

    MS NAUERT: Well, he’s the Vice President. He’s entitled to say whatever he wants to say, and the Vice President and I are certainly in different kinds of positions. I’m not authorized to detail or forecast sanctions, but if the President or the Vice President want to do so, they are certainly more than welcome to.

    QUESTION: (Off-mike.)

    MS NAUERT: Okay, okay. All right, who had North Korea?

    QUESTION: North Korea over here.

    MS NAUERT: Okay. Hi, how are you?

    QUESTION: So this is kind of a follow-up from last time, but Bob Corker – I was at the hearing today – he also was talking about talks with North Korea. So is – my question today is are there talks within the State Department about shaping, like, actual like what we would say to North Korea if we start talks with them?

    MS NAUERT: Okay. So that’s – I think that’s two hypotheticals built in there, but —

    QUESTION: But clearly there’s talks happening about talks at multiple levels on —

    MS NAUERT: So a couple things to that effect. I think the hearing that you’re referring to – by the way, if I may mention, our acting assistant secretary for East Asia Pacific, EAP Susan Thornton, is on the Hill today for her confirmation hearings. Among the things that she said on the Hill for her hearings include this: We’re leaving the door open to engagement. We want engagement to consist of one issue, that is denuclearization. Our policy hasn’t changed; our policy remains the same. The overall goal is denuclearization. The United States and many other countries have this agreement. It is considered a worldwide agreement, not just with us but many other countries, and you’ve seen that echoed at the UN Security Council with four unanimous rounds of sanctions that they voted to pass. To pursue that, we have the maximum pressure campaign. That maximum pressure campaign exists to this day, and virtually every week we are seeing more countries participate or do new things or ratchet the pressure – ratchet up the pressure on North Korea.

    Malaysia, I think I mentioned that the other day; Kuwait not that long ago; Peru, the Secretary referenced that when he was visiting Peru a week or so ago. And the Vice President, when he was just there for his travels, reaffirmed our position. The Secretary has said repeatedly that we are starting to see signs that our maximum pressure campaign is putting strain on North Korea. Now, you’re going to ask, what are those signs, what are the strain that he’s seen? That’s something that we just won’t detail, but we’re keeping a close eye on it.

    If the time comes that we believe that North Korea is serious about talking about denuclearization, we will have a conversation with our partners, with our allies, with our allies in the region, about the appropriate next steps. So we’re not there yet.

    QUESTION: North Korea.

    QUESTION: On Egypt.

    QUESTION: South Korea —

    MS NAUERT: Okay, North – okay. Hold on, Janne, I already called on you. Do you have a North Korea question, ma’am?

    QUESTION: Jehan al-Husaini.

    MS NAUERT: Okay, let me just finish up with —

    QUESTION: On Korea.

    QUESTION: South Korea.

    MS NAUERT: Yeah, let me – hold on. Let me finish up with North Korea and then we’ll go on to another region. Hi, sir, in the back.

    QUESTION: Yes.

    MS NAUERT: What’s your name?

    QUESTION: Ian Talley from the Wall Street Journal.

    MS NAUERT: Oh hey, Ian.

    QUESTION: Hi, how are you doing?

    MS NAUERT: Good. Have you been here before?

    QUESTION: I have been here before, not in your tenure.

    MS NAUERT: Okay. In my tenure, okay. Well, welcome, thanks.

    QUESTION: Thank you. So I have a follow up question on Turkey, which we – should take a later point, but on North Korea, Ms. Thornton said that denuclearization was our preference.

    MS NAUERT: Mm-hmm.

    QUESTION: Does that mean that the U.S. would consider other options?

    MS NAUERT: I don’t think so. I think that the United States and this administration – and I don’t have Susan’s quote in front of me – but I think that that is what we are all working toward, that is our policy, denuclearization. Okay.

    QUESTION: Can I follow up?

    QUESTION: North Korea.

    MS NAUERT: Okay, yeah.

    QUESTION: Yes. So Thornton also mentioned that it’s in her understanding there’s no bloody nose strategy. Could you please elaborate? Has that limited military strike option been discussed at all?

    MS NAUERT: I think we’ve been over that before, or I know I’ve discussed that matter here numerous times. I don’t have Susan’s quote in front of me, so I’m just going to leave it at that. Our policy remains the same that what we’re doing here out of this building is diplomacy. Across the river, they handle other issues, but we handle diplomacy here. Maximum pressure continues and we’ll keep pushing that one.

    QUESTION: Was that strategy being discussed before the Olympic?

    MS NAUERT: Was what strategy?

    QUESTION: (Off-mike.)

    QUESTION: Was the bloody nose option being discussed before the Olympic?

    MS NAUERT: Oh, goodness, not – look, not that I am aware of. That is not what we do out of our building. The State Department, Secretary Tillerson, and even if you talk to Secretary Mattis, they will tell you that diplomacy is the preferred approach, but then it always has to be backed up by a credible military threat. Okay?

    QUESTION: India?

    QUESTION: (Off-mike.)

    MS NAUERT: All right. Okay. All right. We’re going to have to wrap it up in just a second. Hold on.

    QUESTION: India?

    MS NAUERT: Hold on one second.

    QUESTION: (Off-mike.)

    MS NAUERT: Hi. Okay. We’ll do —

    QUESTION: Jehan al-Husaini from Al Hayat newspaper.

    MS NAUERT: Okay, hi.

    QUESTION: It’s regarding to the humanitarian situation in Gaza. It’s very – it’s miserable and it’s very critical. I wonder if there is any American efforts to save Gaza, to lift the siege on Gaza, and how it will be done.

    MS NAUERT: Okay. So a couple things to that: We support an overall peace deal. We also recognize that the state of – that civilians are living in, the state of things in Gaza is miserable. We see that delays for medical treatment, disruptions to electricity are far too common for the people to have to face there.

    We also recognize that that misery is perpetuated by Hamas. Hamas choosing to spend the money that it has – its people’s money – on – not on humanitarian aid, not on taking care of its people, but doing things like building tunnels to get terrorists in to disrupt activity and make life worse for its people. Our diplomats who work for the State Department have been advocating for an increase in Palestinian Authority referrals for Gaza medical patients to actually receive treatment outside of the Gaza strip, recognizing that not everyone is able to get the medical supplies that they need, the hospitals aren’t – hospitals facing those electricity disruptions as well. So we’ve been advocating for that. If I get any new information for you on that, I’d be – certainly be happy to let you know.

    Jason Greenblatt, our international – who is handling our international negotiations on this, he’s been engaging with Israeli authorities regarding medical transfer permits, because I understand that they need permits. And that’s enabled dozens of people so far to receive some cancer treatments. So we’re doing what we can at this point. We believe that there’s no sustainable solution that will be reached until Hamas turns over its administrative control of Gaza to legitimate Palestinian authorities, and we certainly hope that will happen. We’ve seen other nations recently step up and offer additional money to help out people there and we would certainly support that.

    QUESTION: (Off-mike.)

    QUESTION: So you’re calling on the Israelis to increase the number of medical permits?

    MS NAUERT: That’s one of the things that we’re advocating. That’s one of the things we’re advocating. Look, when you’re in a situation where electricity is spotty, where people can’t get the medical equipment, the hospitals can’t get the medical equipment they need and people need lifesaving treatment, we certainly think that they should be able to get that. That, we believe, is just a certain level of decency.

    QUESTION: (Off-mike.)

    QUESTION: (Off-mike.)

    MS NAUERT: Okay. I’m going to have to wrap it up, so —

    QUESTION: (Off-mike.)

    QUESTION: One more on India?

    MS NAUERT: Okay. India, last thing.

    QUESTION: A quick question – two questions, please. One, for the last two years, Prime Minister Modi has been traveling around the globe, including U.S., and recently in the Middle East, including Saudi Arabia and UAE. And over there, he – for the first time ever, Saudi Arabia accepted a UAE to build a – to give a land for a Hindu temple. My question is here: That – you think the region is changing as far as women’s rights and religious freedom, because Secretary has been going there, how do you think – Secretary has been talking about the same thing, religious freedom and women’s rights in the region?

    MS NAUERT: Well, I know that that is a matter overall that the Secretary brings up wherever he goes, whether it’s freedom of religion, women’s rights, freedom of speech – we just saw him talk about that in Egypt. I’m pleased to hear what you’re telling me. I can’t verify it myself, but pleased to hear if that would be the case. We’re seeing lots of parts of the world open up in that regard: recent reports about the women showing up at the soccer stadium in Saudi Arabia, women driving. We think that that is a good thing and we celebrate that. Okay.

    QUESTION: (Off-mike.)

    QUESTION: And finally, Madam, as far as U.S.-India relations are concerned, Secretary’s visit – before his visit, he spoke, of course, of U.S.-India relations at the CSIS and also he opened the door for Madam Ivanka. So where are we going now as far as U.S.-India relations are concerned? Because President spoke with Prime Minister Modi last week —

    MS NAUERT: Yeah.

    QUESTION: — from the White House.

    MS NAUERT: Well, the President – our President, President Trump, certainly has a strong relationship with President Modi[1]. I know that his daughter really enjoyed having been over in Hyderabad late last year, and so it’s an important relationship, an increasingly important relationship. And as you see India doing rebuilding – our friend Nazira right here in front of you, she’s from Afghanistan.

    QUESTION: (Off-mike.)

    MS NAUERT: You’re from India. India is doing a lot of rebuilding and large-scale reconstruction projects in Afghanistan. That is a —

    QUESTION: Two billion dollars.

    MS NAUERT: Two billion dollars, thank you. So who knows that better than you?

    QUESTION: It’s three billion.

    MS NAUERT: How much?

    QUESTION: (Off-mike.)

    QUESTION: Three billion, three billion, three billion.

    MS NAUERT: Three billion, okay. So here we go. This is —

    QUESTION: (Off-mike.)

    MS NAUERT: — a good – (laughter) – but really, this is a good example of the world coming together and working through – places that may not have – countries that may not have worked together in the past. This is an example of how that is now being done: Saudi Arabia helping out in Iraq —

    QUESTION: U.S. —

    MS NAUERT: — India helping out in Afghanistan, all of these places doing that. So I think it’s nice today to leave it on a high note, so thank you very much. I’ll see you back here on Tuesday. Okay.

    (The briefing was concluded at 3:36 p.m.)


    [1] Prime Minister Modi



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Ultrafine and Fine Particle Number and Surface Area Concentrations and Daily Cause-Specific Mortality in the Ruhr Area, Germany, 2009–2014

Author Affiliations open

1Institute of Occupational, Social and Environmental Medicine, Center for Health and Society, Heinrich-Heine-University of Düsseldorf, Düsseldorf, Germany

2Institute of Energy and Environmental Technology e.V., Duisburg, Germany

3Federal Institute of Occupational Safety and Health, Dortmund, Germany

4Center for Nanointegration Duisburg-Essen (CENIDE), University Duisburg-Essen, Duisburg and Essen, Germany

5Department of Epidemiology, Lazio Region Health Service, Rome, Italy

6Institute of Environmental Medicine, Karolinska Institute, Stockholm, Sweden

PDF icon PDF Version (1.3 MB)

  • Background:
    Although epidemiologic studies have shown associations between particle mass and daily mortality, evidence on other particle metrics is weak.
    Objectives:
    We investigated associations of size-specific particle number concentration (PNC) and lung-deposited particle surface area concentration (PSC) with cause-specific daily mortality in contrast to PM10.
    Methods:
    We used time-series data (March 2009–December 2014) on daily natural, cardiovascular, and respiratory mortality (NM, CVM, RM) of three adjacent cities in the Ruhr Area, Germany. Size-specific PNC (electric mobility diameter of 13.3750nm), PSC, and PM10 were measured at an urban background monitoring site. In single- and multipollutant Poisson regression models, we estimated percentage change (95% confidence interval) [% (95% CI)] in mortality per interquartile range (IQR) in exposure at single-day (0–7) and aggregated lags (0–1, 2–3, 4–7), accounting for time trend, temperature, humidity, day of week, holidays, period of seasonal population decrease, and influenza.
    Results:
    PNC100750 and PSC were highly correlated and had similar immediate (lag0–1) and delayed (lag4–7) associations with NM and CVM, for example, 1.12% (95% CI: 0.09, 2.33) and 1.56% (95% CI: 0.22, 2.92) higher NM with IQR increases in PNC100750 at lag0–1 and lag4–7, respectfully, which were slightly stronger then associations with IQR increases in PM10. Positive associations between PNC and NM were strongest for accumulation mode particles (PNC 100500nm), and for larger UFPs (PNC 50100nm). Associations between NM and PNC<100 changed little after adjustment for O3 or PM10, but were more sensitive to adjustment for NO2.
    Conclusion:
    Size-specific PNC (50500nm) and lung-deposited PSC were associated with natural and cardiovascular mortality in the Ruhr Area. Although associations were similar to those estimated for an IQR increase in PM10, particle number size distributions can be linked to emission sources, and thus may be more informative for potential public health interventions. Moreover, PSC could be used as an alternative metric that integrates particle size distribution as well as deposition efficiency. https://doi.org/10.1289/EHP2054
  • Received: 18 April 2017
    Revised: 19 December 2017
    Accepted: 21 December 2017
    Published: 15 February 2018

    Address correspondence to F. Hennig, Universitätsklinikum Düsseldorf, AG Umweltepidemiologie, Postfach 101007, 40001 Düsseldorf, Germany. Telephone: 49 211 586729111. Email: Frauke.hennig@uni-duesseldorf.de

    Supplemental Material is available online (https://doi.org/10.1289/EHP2054).

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Increases of daily fine particulate matter [PM ≤2.5 μm and ≤10 μm, respectively, in aerodynamic diameter (PM2.5 and PM10)] have been shown to be associated with natural mortality (NM) in several North American and European cities (HEI 2010; Katsouyanni and Samet 2009; Samoli et al. 2008). Epidemiological studies have further shown that PM is associated with adverse health effects, such as short- and long-term cardiovascular morbidity and mortality, diseases of the central nervous system, respiratory morbidity, and lung cancer (WHO 2013). Toxicological studies suggest that inhaled ultrafine particles (UFPs) might be particularly harmful, because they can pass the lung epithelium more easily and translocate into the blood to be transported to other organs (Oberdörster et al. 2005). However, epidemiological evidence on pathogenic health effects of UFPs is still limited and inconclusive (HEI Review Panel on Ultrafine Particles 2013; WHO 2013), mainly due to the lack of routinely monitored UFP data and few dedicated measurement campaigns in the framework of specific research projects. UFPs are commonly measured as particle number concentration (PNC), representing more than 85% of the total PM2.5 particle number (Hinds 1999) while contributing little to the PM concentration. The latter is usually the only regulated ambient air particle metric worldwide. Although PM is a mixture composited by different particle sizes and numbers, particles of different size and number concentration are usually generated by different sources (Morawska et al. 1999) such that size and number distribution may provide a better understanding to identify sources as a potential basis for an intervention measure. The commonly used UFPs, defined as particles with an electric diameter <100 nm, for example, combine nucleation and Aitken mode particles (<30 nm and 30–100 nm respectively), whereas combustion-generated particles (from vehicle emissions) range from 30 nm to 500 nm (Vu et al. 2015). UFP concentration alone therefore does not inform about the different sources of the particles.

Another potentially important metric is the integrated measure of lung-deposited surface area concentration of airborne particles (PSC), which takes into account the surface area as well as the size-dependent deposition efficiency of respective particles in the respiratory system. This metric thus constitutes a proxy of the particle’s reactivity, which is related to surface area, as well as its capacity to carry adsorbed chemical species, both possibly promoting oxidative stress, a precursor of inflammatory effects (Hussain et al. 2009). Besides PM, PNC in different size fractions and particle surface area may hence provide a better measurement regarding the toxicity of PM exposure (Noël et al. 2016; Oberdörster 2000) as well as the identification of sources (Morawska et al. 1999).

In a European multicenter analysis on health effects of UFP number on natural and cardiorespiratory mortality including Finland, Sweden, Denmark, Germany, Italy, Spain, and Greece (Stafoggia et al. 2017), a weak delayed effect of UFP was estimated (>lag5). However, this multicenter study was limited by the heterogeneity of its exposure assessment methodology such as instrumentation capturing slightly different size ranges of particles or placement of monitors (background vs. traffic location) as well as by different measurement periods (time and duration) (Stafoggia et al. 2017). A slight difference in the size ranges of measured UFPs due to the use of different instruments has a great impact on the measured overall PNC because the number concentration of particles increases remarkably in the smallest size fractions. Moreover, the location of the monitoring equipment (height and placement of monitors with respect to local sources and the location of the study population) also substantially influence the representativeness of the exposure measurements and might introduce bias as a consequence of differential exposure measurement error (Stafoggia et al. 2017).

In this study we tried to overcome the aforementioned limitations by focusing on one large single study, located in the densely populated German Ruhr Area (Essen, Mülheim, and Oberhausen). Being part of the German Ultrafine Aerosol Network (GUAN) (Birmili et al. 2016), this time-series study benefits from a comparatively long measurement period of almost 6 y and an in-depth characterization of ultrafine (electric mobility diameter <100 nm, including the size ranges 13.3–30, 30–50, and 50–100 nm) and fine (electric mobility diameter 100–750 nm, including the size ranges 100–250, 250–500, and 500–750 nm) particles, including number concentration and lung-deposited PSC, a metric that has rarely been investigated in epidemiological studies to date. In addition, the measurement site is co-located with a central urban background monitoring station of the regional air quality network (Mülheim Styrum), enabling us to also make use of monitored PM10, nitrogen dioxide (NO2), and ozone (O3), which potentially confound or modify ultrafine or fine particle effects on health. In a recent meta-analysis, NO2 and PM2.5 both were associated with mortality in multipollutant analyses (Faustini et al. 2014), whereas O3 is highly correlated with temperature and sunlight, and hence might be an additional or even independent risk factor (Levy et al. 2012). Considering that multiple air pollutants originate from common sources, multiple air pollutants may interact with or confound each other. Results of the European study indicated that associations between UFPs and mortality were confounded by NO2, PM2.5, and PM2.5–10; whereas adjusting for PM10 or O3 had little influence on effect estimates (Stafoggia et al. 2017).

The objective of this study was to investigate the associations of size-specific PNC as well as lung-deposited PSC on natural, cardiovascular, and respiratory mortality in the Ruhr Area, based on a time-series study from March 2009 to December 2014. In addition to the toxicologically important novel particle metrics, we investigated the role of copollutants such as PM10, NO2, and O3.

Methods

Mortality Data

We collected daily mortality counts based on the primary cause of death, defined as natural [International Classification of Diseases, 10th Revision (ICD-10) A00–R99], cardiovascular (ICD-10 I00–I99) and respiratory (ICD-10 J00–J99) mortality in the three adjacent cities of Essen, Mülheim, and Oberhausen between January 2008 and December 2014 from the central statistical and IT services provider of North Rhine-Westphalia. The three adjacent cities (in an area of ∼379 km2) with a total of nearly 1 million inhabitants [Essen, ∼580,000 (210 km2); Mülheim, ∼170,000 (91 km2); and Oberhausen, ∼211,000 (77 km2)] are located in the western part of the metropolitan Ruhr Area. As respective outcomes, we used the sum of city-specific natural and cause-specific deaths per day. The primary cause of deaths was assigned based on the underlying disease instead of the immediate cause of death.

Air Pollution Data

Exposure data was collected at the project-specific measurement site (i.e., GUAN) provided by the Institute of Energy and Environmental Technology (IUTA), co-located to an urban background monitoring site of the regional air quality network (code “STYR”) operated by the North Rhine-Westphalia State Agency for Nature, Environment and Consumer Protection [Landesamt für Natur, Umwelt und Verbraucherschutz (LANUV) North Rhine-Westphalia (NRW), Essen, Germany] from March 2009 until December 2014. A detailed description of the measurement site and respective measurement techniques can be found elsewhere (Birmili et al. 2016). The measurement site is located close to the administrative border of the cities of Mülheim and Oberhausen. Within a 1-km buffer, the site is surrounded by highways with channel-like cross-sections (∼10 m below site level) and traffic of approximately 50,000 vehicles/day (∼250 m north), a railway yard (south/southwest), and a medium-trafficked street and its junction with a highway exit (west/northwest). Main wind directions are south/southwest and northeast. The mixed residential-, industrial-, and traffic-influenced character of the site is typical for many urban areas in the Ruhr Area and hence believed to be representative for a large part of the population living in the adjacent cities of Mülheim and Oberhausen, including their eastern neighbor Essen.

Measured particle characteristics included size-specific PNC of ultrafine, fine, and coarse particles (as well as their PSC) that deposit in the alveolar or tracheobronchial region of the lung (short: lung-deposited PSC). PNCs [number per cubic centimeter (n/cm3)] were measured with a scanning mobility particle sizer (TSI Inc.) in the size ranges of 13.3–750 nm electrical mobility diameter (Wang and Flagan 1990). In an effort to understand the health effects of different particle sizes, potentially generated by different emission sources and reaction processes, we investigated six particle size fractions, including particles size ranges of 13.3–30 nm (reflecting the nucleation mode: <30 nm), 30–50 nm, 50–100 nm (reflecting the Aitken mode: 30–100 nm), 100–250 nm, 250–500 nm, and 500–750 nm (reflecting the accumulation mode: 100–1,000 nm). The PSC of lung-deposited particles with a diameter of 20–1,000 nm was measured in micrometers squared per cubic centimeter every second using a nanoparticle surface area monitor (NSAM; model 3550, TSI Inc.) (Asbach et al. 2009). The NSAM uses an opposed flow unipolar diffusion charger followed by an ion trap to remove excess ions. Particles >1 μm are withheld by means of an impactor located at the NSAM entrance. The voltage in the ion trap can be adjusted to manipulate the particle size distribution and therefore the response function; that is, if the ion trap voltage is set to 200 V, the NSAM delivers the surface area deposited in the alveolar region, whereas it delivers the surface area of particles deposited in the tracheobronchial region when the voltage is set to 100 V. In our study, alveolar-deposited particles were monitored. The accuracy of surface determination decreases substantially for particle diameters below 20 nm and above 400 nm (Asbach et al. 2009). However, typical outdoor aerosol particles <20 nm in diameter and >400 nm in aerodynamic diameter contribute little to the total surface area.

Routinely monitored air pollutants at the central state-run (LANUV) monitoring site (STYR) included PM10 (β-attenuation), NO2 (chemiluminescence method), and O3 (ultraviolet absorption).

Covariates

Daily temperature [in degrees Celsius (°C), daily mean] and relative humidity were measured according to standardized protocols (VDI-guidelines 3786, parts 3 and 4; Verein Deutscher Ingenieure 2009, 2012) at a state-run monitoring site (Duisburg-Walsum), located 11 km northeasterly from the study site. External information on periods of influenza was collected from the central statistical and information technology services provider of North Rhine-Westphalia. In addition, we defined an indicator for population decrease during summer, following the definition in Stafoggia et al. (2017): namely a three-level variable assuming value “1” for the time of school holidays in North Rhine-Westphalia (6 wk within July and September; e.g., 9 July until 21 August in 2012 or 22 July until 3 September in 2013), and “2” in the 4-wk period around the school holidays; all other days stood for reference days and were assigned to “0”). Further variables included day of week (six indicator variables, with Sundays as the reference category), holiday (an indicator variable identifying the main bank holidays in North Rhine-Westphalia), and season (fall=September–November; spring=March–May, summer=June–August; and winter=December–February).

Statistical Analysis

The basic description of particle metrics, mortality, and meteorological data included visualizations of the time series, median [interquartile range (IQR)], and Spearman’s correlation coefficients between respective exposure variables.

To estimate associations between exposures and daily cause-specific mortality, we used Poisson regression models allowing for overdispersion. Regression models included penalized regression splines as a smoothing function for time trend. We further included potential confounders based on a review of current literature (Stafoggia et al. 2017). Adjusted models included mean air temperature [day of death (lag0) and a moving average of 1–3 d prior to the observed death (lag1–3)], relative humidity, and indicator variables for day of the week, holidays, influenza epidemics, and the presence of a population decrease in the respective cities during the summer vacation period. Air temperature was modeled by fitting a natural cubic regression spline to allow for nonlinear confounder adjustment.

We investigated single-lags from the same day of death (lag0) up to 7 d prior to death (lag7). Moreover, we investigated aggregated lags, representing immediate effects (0–1 d prior to the death; lag0–1), medium-term effects (lag2–3), and delayed effects (lag4–7). We chose single-lag models as well as aggregated 2- to 4-d lags over distributed lag-models because of multiple missing data in the PNC series and the respective loss of power, especially in the underlying small study population. By ending up with 11 models per investigated pollutant, we aimed to look for a general pattern of associations rather than identifying adverse health effects based on single-day lags that could be observed in such a multiple testing situation.

The main exposure metrics of interest were size-specific PNC, aggregated as ultrafine (PNC<100) and fine particles (PNC100–750), as well as PSC and PM10. In addition, we also investigated size-specific PNC in finer resolved size fractions (PNC13.3–30, PNC30–50, PNC50–100, PNC100–250, PNC250–500, and PNC500–750). All health effect estimates are presented as mean percentage increase [95% confidence interval (CI)] [% (95% CI)] in mortality per IQR of the respective exposure.

We calculated two-pollutant models in order to investigate whether results for UFPs (PNC<100) were independent of other pollutants or metrics: a) PNC<100 and PM10, b) PNC<100 and NO2, c) PNC<100 and O3, d) PNC<100 and PNC100–750, and e) PNC<100 and PSC. In addition we investigated two-pollutant models including a) PNC100–750 and PM10, b) PNC100–750 and NO2, c) PNC100–750 and O3, and d) PNC100–750 and PSC.

Furthermore, we investigated effect modification of UFPs and particles (PNC100–750) by cold and warm periods of the year (October–March vs. April–September), and by high or low concentration of PM10, O3, NO2 and PSC by including interaction terms between the potential effect modifier and the exposure of interest. High levels of PM10, O3, NO2, and PSC referred to concentrations above the 75th percentile of the respective distribution. Effect modification was checked based on a 5% significance level regarding the coefficient of the respective interaction term.

Results

Because particle metrics (PNC and PSC) were only measured beginning in March 2009, our analysis was based on the time period from March 2009 until December 2014 (2,132 d). We observed different missing patterns among exposures ranging from 266 missing days for PNC, 125 d for PSC, 110 d for O3, and 91 d for NO2 to 29 d for PM10. The majority of missing exposure data for PNC resulted from a sampling pump failure of the scanning mobility particle sizer during specific time windows (data not shown) and hence was assumed to be missing at random. Because of different missing patterns, the number of observations slightly changed between the analysis for each metric and lag.

Medians (IQRs) of daily cause-specific mortality per approximately 946,000 inhabitants in Essen, Mülheim, and Oberhausen were 32 (8) death/day for natural, 12 (5) for cardiovascular (corresponding to 37.5% of the overall deaths), and 3 (2) for respiratory mortality (corresponding to 9.4% of the overall deaths) (Table 1 and Figure 1). The city of Essen contributed most to the observed mortality (approximately 60%). Median (IQR) PNC of UFPs (PNC<100) was 9,871 n/cm3 (4,900), with the smallest size fraction (PNC13.3–30) contributing the most to PNC (4,623 n/cm3; 2,438). Median PSC was 36.1 μm2/cm3 (21.7) and PM10 was 20.2 μg/m3 (13.3), which is well below the European annual limit value of 40 μg/m3. In total, the PM10 24-h limit (50 μg/m3; EU 2008) was exceeded on 108 d (Figure 1). The median for NO2 was 29.2 μg/m3 (16.2), which was also below the annual limit value of 40 μg/m3. The median temperature was 11.9°C (9.9), and relative humidity 78.8% (18.5).

 

Table 1. Median (IQR) daily mortality, particle metrics, and meteorology in the Ruhr Area (Essen, Mülheim, and Oberhausen) between March 2009 and December 2014 (2,132 days).
Variable Median (IQR) Days (n)a
Mortality
 Naturalb 32.0 (8.0) 2,132
 Cardiovascularc 12.0 (5.0) 2,132
 Respiratoryd 3.0 (2.0) 2,132
Exposure PNC (n/cm3)
 PNC13.3–30 4,623.1 (2438.2) 1,866
 PNC30–50 2,673.1 (1492.5) 1,866
 PNC50–100 2,368.7 (1608.7) 1,866
 PNC<100 (UFP) 9,870.6 (4900.2) 1,866
 PNC100–250 1,209.7 (903.2) 1,866
 PNC250–500 195.8 (180.8) 1,866
 PNC500–750 9.0 (14.0) 1,866
 PNC100–750 (FP) 1,437.3 (1060.9) 1,866
PSC (μm2/cm3) 36.1 (21.7) 2,007
 PM10 (μg/m3) 20.2 (13.3) 2,103
 NO2 (μg/m3) 29.2 (16.2) 2,041
 O3 (μg/m3) 54.0 (37.0) 2,022
Meteorology
 Temperature (°C) 11.9 (9.9) 2,124
 Relative humidity 78.8 (18.5) 2,124

aThe number of days differs because of inconsistencies in measurements.

bEssen: 19.0 (6.0); Oberhausen 7.0 (4.0); Mülheim: 5.0 (3.0).

cEssen: 7.0 (4.0); Oberhausen 2.0 (3.0); Mülheim: 2.0 (2.0).

dEssen: 2.0 (2.0); Oberhausen 0.0 (1.0); Mülheim: 0.0 (1.0).

Figure 1 shows eight time series from March 2009 until December 2014.
Figure 1. Time series of daily cause-specific mortality (top left panel: natural mortality is shown in black, cardiovascular mortality is shown in gray, and respiratory mortality is shown in dark gray), PNC<100, PNC100–750, PSC, PM10 (top right panel: the dashed horizontal line indicates the 24-h limit of 50 μg/m3), NO2, O3, and temperature in the Ruhr Area. Note: NO2, nitrogen dioxide; O3, ozone; PM10, particulate matter ≤10 μm in aerodynamic diameter; PNC<100, size-specific particle number concentration of particles <100 nm electrical mobility diameter; PNC100–750, PNC of particles with 100–750 nm electrical mobility diameter; PSC, particle surface area concentration.

Spearman correlation (r) between air pollutants ranged from −0.39 (for NO2 and O3) to 0.99 (PNC100–250 and PNC100–750) (Table 2; based on data for 1,669 d with complete measurement data for all exposure metrics and pollutants.). PNC<100 (UFPs) generally correlated moderately with PSC and NO2 (r=0.63 and r=0.42), and correlated considerably more weakly with PM10 and O3 (r=0.26 and r=0.14). The smallest size fraction (PNC13.3–30) correlated weakly with other particle metrics and pollutants (0.00≤r≤0.28). PNC100–750 revealed overall high correlations with the particle metrics PSC (r=0.94) and PM10 (r=0.74). PNC100–750 correlated slightly weaker with NO2 than PNC<100 (r=0.65), whereas no correlation was observed between PNC100–750 and O3.

 

Table 2. Correlation coefficients (Spearman r) between exposure metrics and pollutants (n=1,669) in the Ruhr Area between March 2009 and December 2014 based on daily-based complete case data for all exposures, n=1,669.
PNC<100 PNC100–750 PNC13.3–30 PNC30–50 PNC50–100 PNC100–250 PNC250–500 PNC500–750 PSC PM10 NO2
PNC<100 1
PNC100–750 0.56 1
PNC13.3–30 0.86 0.25 1
PNC30–50 0.92 0.53 0.67 1
PNC50–100 0.79 0.85 0.43 0.82 1
PNC100–250 0.60 0.99 0.28 0.57 0.87 1
PNC250–500 0.27 0.82 0.03* 0.26 0.56 0.74 1
PNC500–750 0.21 0.71 −0.01* 0.21 0.49 0.64 0.9 1
PSC 0.63 0.94 0.28 0.66 0.90 0.93 0.76 0.68 1
PM10 0.26 0.74 0.00* 0.28 0.55 0.69 0.82 0.81 0.73 1
NO2 0.42 0.65 0.22 0.41 0.57 0.63 0.59 0.58 0.70 0.63 1
O3 0.14 −0.01* 0.15 0.13 0.06 0.03* −0.19 −0.25 −0.04* −0.12 −0.39

*p>0.05, all other p≤0.05.

Main Effects

Estimated associations of exposure with mortality showed different patterns for the different particle metrics and causes of mortality (Figure 2). Overall patterns of PNC100–750 and PSC were similar and comparable to those of PM10, showing immediate (lag0–1) and delayed (lag4–7) associations for NM and cardiovascular mortality (CVM) (Figure 2). Point estimates for immediate associations (lag0–1) of PNC100–750 were 1.12% (95% CI: −0.09, 2.33) for NM and 1.63% (95% CI: −0.40, 3.71) for CVM (see Table S1), and for more delayed associations (lag4–7) 1.56% (95% CI: 0.22, 2.92) for NM and 0.89% (95% CI: −0.43, 3.27) for CVM (see Table S1). These effect estimates were slightly stronger than those of PM10 on an IQR basis with an immediate (lag0–1) increase in NM and CVM of 0.67% (95% CI: −0.29, 1.64) and 0.99% (95% CI: −0.63, 2.65) or a more delayed (lag4–7) increase in NM and CVM of 0.97% (95% CI: −0.13, 2.09) and 0.75 (95% CI: −1.13, 2.67) (Figure 2; see also Table S1). We did not observe clear associations between PNC<100 (UFP) and NM or CVM, although the observed pattern suggested a more delayed association (lag4–7) with a slightly higher point estimate of 2.01% (95% CI: −1.41, 5.55), yet estimated less precisely (Figure 2; see also Table S1). For respiratory mortality (RM) we observed comparatively strong single-day associations at lag2 and lag6 with PNC<100 of 3.50% (95% CI: −0.77, 7.95) and 4.51% (95% CI: 0.37, 8.81), respectively. However, there were no conclusive patterns linking RM with aggregated lag-exposures of the considered pollutants.

Figure 2 shows twelve plots showing the percentage difference (95 percent confidence interval) of natural mortality, cardiovascular mortality, and respiratory mortality (y-axis) across single and aggregated lags (x-axis) estimated for particle metrics, namely, PNC subscript less than 100, PNC subscript 100 to 750, PSC, and PM subscript 10.
Figure 2. Short-term associations per IQR increase of air pollutant concentration and daily natural and cause-specific mortality in the Ruhr Area between March 2009 and December 2014, estimated for different particle metrics (PNC<100, PNC100–750, PSC, and PM10) at single-day lags (lag0–lag7) and for aggregated lags (lag0–1, lag2–3, lag4–7) in Poisson regression models, adjusted for time trend, temperature, humidity, day of week, holidays, period of seasonal population decrease, and influenza. (Corresponding numeric data are provided in Table S1.) Note: IQR, interquartile range; NO2, nitrogen dioxide; PM10, particulate matter ≤10 μm in aerodynamic diameter; PNC<100, size-specific particle number concentration of particles <100 nm electrical mobility diameter; PNC100–750, PNC of particles with 100–750 nm electrical mobility diameter; PSC, particle surface area concentration.

When looking at size-specific associations in more detail (Figure 3; see also Table S2), we observed immediate inverse associations of PNC13.3–30 with NM and CVM (−1.81% (95% CI: −3.30, −0.30) and −1.63% (95% CI: −4.16, 0.97), respectively; whereas for lag4–7, the estimate for NM moved close to the null and that for CVM was positive (95% CI: −0.55% (−2.40, 1.34) and 1.43% (95% CI: −1.86, 4.83) respectively). In contrast, patterns for PNC with an electric diameter >50–500 nm pointed to positive immediate (lag0–1) and delayed (lag4–7) associations with NM and CVM, similar to associations of PNC100–750, PSC and PM10. Clearest associations were observed for particles of 100–250 and 250–500 nm size and NM. For RM, patterns were less conclusive, yet somewhat different from NM and CVM, indicating only delayed associations with larger particles (electric diameter >250 nm).

Figure 3 shows eighteen plots showing the percentage difference (95 percent confidence interval) of natural mortality, cardiovascular mortality, and respiratory mortality (y-axis) across lags (x-axis) estimated for particle metrics, namely, PNC 13.3 to 30, PNC 30 to 50, PNC 50 to 100, PNC 100 to 250, PNC 250 to 500, and PNC 500 to 750.
Figure 3. Short-term associations (lag0–1, lag2–3, lag4–7) per IQR increase of size-specific particle number concentrations and daily natural and cause-specific mortality in the Ruhr Area between March 2009 and December 2014, estimated in Poisson regression models, adjusted for time trend, temperature, humidity, day of week, holidays, period of seasonal population decrease, and influenza and presented as percentage differences (95% confidence interval) [% (95% CI)] in mortality. (Corresponding numeric data are provided in Table S2.) IQR, interquartile range.

Adjustment for Copollutants

Effect estimates for NM and CVM in association with PNC<100 and PNC100–750 were similar after adjustment for O3 (Figure 4). In general, effect estimates were mostly robust towards adjustment for PM10, though associations between lag 4–7 PNC<100 and NM became more negative. Adjustment for NO2 on the other hand showed a slightly different pattern: Although effect estimates for UFP on CVM were unaffected by NO2 adjustment, effect estimates for PNC<100 and NM became more negative over all considered lags. Effect estimates for PNC100–750 on both NM and CVM were essentially unchanged after NO2 adjustment. After adjustment for PSC or PNC100–750, associations for PNC<100 and NM or CVM were similar to those adjusted for NO2. Associations between PNC100–750 and both outcomes at lag0–1 became more positive with adjustment for PSC, whereas the association between PNC100–750 and CVM at lag4–7 became negative, although confidence intervals were wide.

Figure 4A shows six plots for PNC subscript less than 100 showing the percentage difference (95 percent confidence interval) of natural mortality and cardiovascular mortality (y-axis) across main exposure metrics, PM subscript 10, O subscript 3, PNC subscript greater than 100, and PSC (x-axis) for lag 0 to 1, lag 2 to 3, and lag 4 to 7. Figure 4B shows the same data for PNC subscript 100 to 750.
Figure 4. Effect estimates for percentage differences (95% confidence interval) [% (95% CI)] in natural and cardiovascular-specific mortality in the Ruhr Area between March 2009 and December 2014 per IQR increase in (A) ultrafine particles (PNC<100) and (B) fine particles (PNC100–750, short: PNC>100) for averaged lags (lag0–1, lag2–3, lag4–7), estimated in Poisson regression models, adjusted for time trend, temperature, humidity, day of week, holidays, period of seasonal population decrease, and influenza with additional adjustment for PM10, NO2, O3, PNC>100 (PNC<100), and PSC. Note: IQR, interquartile range; NO2, nitrogen dioxide; O3, ozone; PM10, particulate matter ≤10 μm in aerodynamic diameter; PNC<100, size-specific particle number concentration of particles <100 nm electrical mobility diameter; PNC100–750, PNC of particles with 100–750 nm electrical mobility diameter; PSC, particle surface area concentration.

Associations between PNC13.3–30 and mortality remained unchanged after adjustment for other metrics (see Figure S1), consistent with expectations given the weak correlations with other pollutants (Table 2).

Effect Modification

Effect modification of associations between fine or ultrafine PNCs and natural or CV mortality were significant only for NM in association with O3 and PNC<100 at lag4–7 (interaction p=0.03), where PNC<100 was positively associated with NM when O3 was below the 75th percentile (1.31%; 95% CI: −0.46, 3.11), and negatively associated with NM when O3 was high (−1.94%; 95% CI: −4.63, 0.83) (Figure 5; see also Table S3). A similar pattern was observed for CVM in association with high or low O3 and PNC<100 at lag0–1 (interaction p=0.03). We did not observe significant (defined as interaction p<0.05) effect modification by season or higher levels of co-exposure (PM10, NO2, or PSC) regarding associations between fine or ultrafine PNCs and NM or CVM. However, at lag4–7, point estimates for PNC<100 were positive among those with lower levels of PM10, NO2, and PSC, but closer to the null among those with higher levels of co-exposure (interaction p: 0.17–0.67). Similarly, for NM and CVM, associations with PNC100–750 at lag0–1 were stronger for those with higher versus lower levels of PM10, NO2, and PSC co-exposure (interaction p=0.15–0.72). The effect estimate between lag2–3 PNC<100 and CVM was positive during the warmer season (April–September, 2.30%; 95% CI: −1.28, 6.06) but negative during colder months (October–March, −2.07%; 95% CI: −5.44, 1.43; interaction p=0.08).

Figure 5A shows six plots for PNC subscript less than 100 showing the percentage difference (95 percent confidence interval) of natural mortality and cardiovascular mortality (y-axis) across season, PM subscript 10, N O subscript 2, O subscript 3, and PSC (x-axis) for lag 0 to 1, lag 2 to 3, and lag 4 to 7. Figure 5B shows the same data for PNC subscript 100 to 750. The data are shown for warm (April to September) (high; greater than 75th percentile) and cold (October to March) (low; less than 75th percentile) periods of the year.
Figure 5. Estimated effect modification by season and copollutants for short-term (lag0–1, lag2–3, lag4–7) percentage differences in natural and cardiovascular mortality based on an IQR increase in the ultrafine particle concentration (PNC<100) in the Ruhr Area between March 2009 and December 2014 using Poisson regression models, adjusted for time trend, temperature, humidity, day of week, holidays, period of seasonal population decrease, and influenza. (Corresponding numeric data are provided in Table S3.) Note: IQR, interquartile range; PNC<100, size-specific particle number concentration of particles <100 nm electrical mobility diameter; PNC100–750, PNC of particles with an electrical mobility diameter between 100 and 750 nm.

Discussion

Our findings suggest that short-term exposures to lung-deposited PSC and PNC in the ultrafine (electric mobility diameter <100 nm) and fine (100–750 nm) particle size ranges (especially PNC 50–500 nm), are associated with small increases in daily NM and CVM. Associations suggested immediate (lag0–1) and slightly delayed (lag4–7) effects, and effect estimates were more precise for all NM than for the smaller subset of deaths due to cardiovascular disease. Associations of size-specific PNC were mostly robust to the adjustment for PM10 and O3, and slightly changed when adjusted for NO2. Effect estimates for PNC100–750 and PSC were similar to those observed for PM10, suggesting immediate as well as delayed effects on NM and CVM. Based on an IQR increase in respective exposure concentration, positive associations for PNC in the 50–500 nm range were stronger than positive associations for PM10.

In this study, we were able to investigate size-dependent PNC, including three size fractions in the UFP size range (13.3–30, 30–50, 50–500 nm) and three size fractions in the fine range (100–250, 250–500, 500–750 nm), aiming to identify the most pathogenic size fraction. We observed that the PNC of the smallest size ranges (13.5–50 nm) was inversely associated with natural and cause-specific mortality. This immediate inverse association of UFPs with natural and cause-specific mortality has been observed before in a German time-series study, showing inverse associations at lag1 and lag2, mainly driven by the smallest particle size, yet less pronounced than shown in our results (Stölzel et al. 2007). In contrast to the inverse association of the smallest size fraction, we observed positive immediate and delayed associations between UFP with an electric mobility diameter of 50–100 nm and daily mortality, which were similar to associations of other fine particle metrics (PNC100–750, PSC, and PM10). Among the fine to submicrometer particle size fractions (PNC100–750), particles with an electric mobility diameter from 100 to 250 and 250 to 500 nm revealed the clearest health effect estimates. Moreover, and in contrast to the inverse immediate associations, UFPs indicated delayed associations with CVM, as has been reported by others (Lanzinger et al. 2016; Stafoggia et al. 2017).

Typically, specific size ranges are related to major emission sources. Particles in the nucleation mode (<30 nm) reflect mainly new particles formed by gas-to-particle conversion, including particles originating from gaseous precursors in vehicle exhaust such as NO2 (Vu et al. 2015). Particles in the Aitken (30–100 nm) and accumulation (100 nm–1 μm) mode with an electric mobility diameter of 30–500 nm contain soot particles from combustion processes, including coal burning power plants, oil combustion, and combustion-engine powered vehicles (Vu et al. 2015). The modal size of vehicle-generated soot particles is in the size range of 100–250 nm (Harrison et al. 2010). Moreover, the particle size fraction 50–250 nm contains diesel exhaust particles, which have been shown to be specifically pathogenic in experimental settings (Mills et al. 2007). Particles from gasoline-powered engines, on the other hand, are typically smaller than diesel soot and mainly form particles <80 nm (Vu et al. 2015). Particles from mechanical abrasion processes such as brake, tire, and road wear are larger and can be found in the accumulation and coarse (>1 μm) mode (Vu et al. 2015). Moreover, accumulation mode particles encompass mostly long-range transported aerosols, whereas nucleation and Aitken mode particles usually have short lifetimes. From a biologic point of view, particles below 50 nm have the highest deposition efficiency, whereas Aitken and specifically accumulation mode particles deposit less efficiently (Kreyling et al. 2006). Moreover, particles below 50 nm contain a higher amount of soluble constituents.

Based on our findings, which show the largest associations for particles sized 50–500 nm, we concluded that primary combustion-generated soot particles might be more harmful than secondary particles formed via nucleation and condensation. This poses the question of whether the PNC in the size range from 50 to 500 nm might actually be a more important metric than the commonly used UFPs, which are defined as particles with a diameter <100 nm.

The repeatedly observed inverse associations for UFP (PNC<100) in temperature- and humidity-adjusted models seemed to be driven by the smallest particle size fraction (13.5–30 nm) and remained striking. From a biologic point of view, it seems implausible that the particles contained in the nucleation mode have a true protective effect on mortality. Associations with PNC<100 at lag0–1 remained inverse when additionally adjusted for NO2 and O3 in separate models, and they could not be explained through any investigated effect modification. In fact, point estimates became even more negative when O3 was below the 75th percentile.

Most time-series studies on short-term mortality effects of UFPs today have conducted single pollutant analyses. The important question remains, whether the observed effects of ultrafine or any other specific particle size fraction act independently of other pollutants considering that they are sharing potential sources. The answer to this question is of great interest with regard to the regulation of exposure and prevention of adverse health effects. In our study, inverse associations between UFP and natural and cause-specific mortality were robust to adjustment for O3 or PM10, but tended to move further from the null (i.e., became more negative) with adjustment for NO2, PNC100–750, or PSC. Similar patterns of for UFP-associations have been observed after adjustment for NO2, and also for PM2.5 before (Stafoggia et al. 2017), whereas others reported associations between prolonged exposure to UFP independent of particle mass exposures (Lanzinger et al. 2016). These contrary findings probably reflect important differences across studies caused by the different mixture of particles and sources due to the region of interest.

The rarely investigated lung-deposited PSC showed similar results as PNC100–750 or PM10, namely immediate and delayed associations with NM and CVM. Moreover, PSC correlated highly (>0.7) with PNC of particles sized 50–500 nm, which were the size-classes revealing the most clearly observed (immediate and delayed) health effect estimates.

Despite a strong correlation between PNC100–750, PM10 and PSC, PSC constitutes an integrated measure of reactive particle surface and deposition efficiency, which serve as a better marker understanding effect mechanisms between the inhalation of particles and health outcomes than solely mass-based or number-based metrics. It has been discussed that particle area surface plays a greater role in oxidative stress and pro-inflammatory effects than particle mass or particle number because the surface is the relevant location for oxidative processes (Hussain et al. 2009). Within this study, however, we were not able to disentangle biological effects of the mass, the number, and the surface of particles.

Season did not affect effect estimates of UFPs in the Ruhr Area consistently in terms of lag-time and cause of mortality, although season clearly affected effect estimates of UFPs on natural and cause-specific mortality and hospital admissions in other European regions (Samoli et al. 2016; Stafoggia et al. 2017). However, in comparison with the Mediterranean climate, the Ruhr Area has a more temperate climate with cool summers and mild and rainy winters, not displaying the strong seasonal pattern observed in Italy or Greece. Overall, we did not observe a consistent pattern among selected effect modifiers regarding associations between fine or ultrafine PNCs and natural or CV mortality.

Overall, our results are in line with results of other time-series studies, showing immediate (lag 0–1) and delayed effects (≥lag 4) of fine particles, while observing more delayed effects of UFPs on natural and cause-specific mortality (Breitner et al. 2009; Ibald-Mulli et al. 2002; Lanzinger et al. 2016; Stafoggia et al. 2017; Stölzel et al. 2007; Wichmann and Peters 2000). One of the first studies on UFPs reported the largest associations between UFPs and nonaccidental mortality for delayed (lag4) exposures in Erfurt, Germany (Wichmann et al. 2000). These results were confirmed in a reanalysis of an extended data base (Breitner et al. 2009; Stölzel et al. 2007). A European study including five cities (Augsburg, Chernovtsy, Dresden, Ljubljana, and Prague) reported an increase in respiratory mortality after 6 d (lag0–5) (Lanzinger et al. 2016). Another European study including eight cities (Helsinki, Stockholm, Copenhagen, Ruhr Area, Augsburg, Rome, Barcelona, and Athens) observed weak delayed associations (lag5–7) with NM and cardiovascular and respiratory mortality (Stafoggia et al. 2017). In contrast, several large multicenter time-series studies on fine particle mass showed primarily immediate effects on daily mortality (HEI 2010; Katsouyanni and Samet 2009; Samoli et al. 2008). Possible biological explanations for these different temporal patterns between size-specific particles could be local inflammation induced by fine particles in the bronchi and lung tissue, which may lead to immediate effects on mortality. In contrast, smaller particles such as UFPs may partly escape pulmonary clearing mechanisms, translocate across biologic membranes, and gain access to the vasculature and systemic circulation, stimulating systemic inflammatory mechanisms. This process can lead to an increased risk for cardiovascular events after several days. The overall reported delayed associations of UFPs and cardiovascular health seem plausible from this biological perspective. Supporting our findings, Stölzel et al. (2007) reported slightly higher delayed effect estimates with CVM than with NM for the UFPs.

Several limitations should be acknowledged in our study. The most obvious one is the small number of mortality events, limiting the statistical power of our results, especially regarding cause-specific mortality. Moreover, we have fitted several models to estimate adverse health effects of multiple pollutants regarding multiple lags and time windows, yielding a higher possibility of rejecting a null effect. However, in this study we aimed to identify a temporal pattern of different sized particles on the different causes of death instead of focusing on associations of single-day lags. In addition, this study used only one monitor as the reference exposure for three adjacent cities. Although PM10 and PM2.5 tend to be more homogeneously distributed over wider spatial regions with daily changes primarily dependent on meteorology, daily UFP concentration changes might differ considerably depending on location and local sources, especially in proximity to major roads or highways (Cyrys et al. 2008; Pekkanen and Kulmala 2004). For our study we assumed that the central monitor, placed at an urban background station, properly captured the day-to-day variability relevant for the surrounding population, as was assumed by others as well (Cyrys et al. 2008). Moreover, the high correlation of several exposure metrics limited our power to disentangle individual metric effects. Another limitation includes the lack of daily measurements of PM2.5, which has been shown to confound health effects of UFPs (Stafoggia et al. 2017).

The main strength of this study is the consistent exposure assessment throughout the study period of approximately 6 y. Furthermore, the study benefits from an in-depth characterization of particles, with the aim to specifically capture toxicologically important particle characteristics, including size-specific PNC and total lung-deposited PSC, a metric that has rarely been investigated in epidemiological studies to date. Moreover, the measurement site was located next to a routine monitoring site, enabling us to make use of monitored copollutants such as PM10, NO2, or O3, which can potentially confound or modify UFP effects on health.

Conclusions

Size-specific PNC (50–500 nm) and lung-deposited PSC indicated an association with NM and CVM in the Ruhr Area, showing immediate (lag0–1) and delayed (lag4–7) effect estimates revealing slightly higher point estimates than these of PM10 based on an IQR increase of exposure concentration. Although results from PM, PNC, and PSC could not be disentangled, it might be beneficial to investigate particle number size distributions, which can be linked to emission sources, in addition to the particle mixture captured by the measurement of PM10 only. Moreover, PSC could be used as an alternative metric that integrates particle size distribution as well as deposition efficiency. Further investigations are needed to establish the different temporal patterns among different particles sizes and surfaces.

Acknowledgments

We thank the Central statistical and IT services provider of North Rhine-Westphalia (Information und Technik NRW, Düsseldorf, Germany) and the North Rhine-Westphalia State Agency for Nature, Environment and Consumer Protection (Landesamt für Natur, Umwelt und Verbraucherschutz (LANUV) NRW, Essen, Germany) for providing, respectively, mortality and exposure data for the three cities of the Ruhr Area. We also thank D. Sugiri for the data management.

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Estimating Acute Cardiovascular Effects of Ambient PM2.5 Metals

Author Affiliations open

1Department of Environmental Health, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA

2Department of Epidemiology, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA

3School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia, USA

4Atmospheric Research & Analysis, Inc., Cary, North Carolina, USA

5Department of Biostatistics and Bioinformatics, Rollins School of Public Health, Emory University, Atlanta, Georgia, USA

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  • Background:
    Few epidemiologic studies have investigated health effects of water-soluble fractions of PM2.5 metals, the more biologically accessible fractions of metals, in their attempt to identify health-relevant components of ambient PM2.5.
    Objectives:
    In this study, we estimated acute cardiovascular effects of PM2.5 components in an urban population, including a suite of water-soluble metals that are not routinely measured at the ambient level.
    Methods:
    Ambient concentrations of criteria gases, PM2.5, and PM2.5 components were measured at a central monitor in Atlanta, Georgia, during 1998–2013, with some PM2.5 components only measured during 2008–2013. In a time-series framework using Poisson regression, we estimated associations between these pollutants and daily counts of emergency department (ED) visits for cardiovascular diseases in the five-county Atlanta area.
    Results:
    Among the PM2.5 components we examined during 1998–2013, water-soluble iron had the strongest estimated effect on cardiovascular outcomes [R͡R=1.012 (95% CI: 1.005, 1.019), per interquartile range increase (20.46 ng/m3)]. The associations for PM2.5 and other PM2.5 components were consistent with the null when controlling for water-soluble iron. Among PM2.5 components that were only measured during 2008–2013, water-soluble vanadium was associated with cardiovascular ED visits [R͡R=1.012 (95% CI: 1.000, 1.025), per interquartile range increase (0.19 ng/m3)].
    Conclusions:
    Our study suggests cardiovascular effects of certain water-soluble metals, particularly water-soluble iron. The observed associations with water-soluble iron may also point to certain aspects of traffic pollution, when processed by acidifying sulfate, as a mixture harmful for cardiovascular health. https://doi.org/10.1289/EHP2182
  • Received: 10 May 2017
    Revised: 5 October 2017
    Accepted: 8 December 2017
    Published: 15 February 2018

    Address correspondence to D. Ye, 1518 Clifton Rd. NE, CNR 2036, Atlanta, GA 30322 USA. Telephone: (203) 848-0225. Email: dongni.ye@emory.edu

    Supplemental Material is available online (https://doi.org/10.1289/EHP2182).

    The authors declare they have no actual or potential competing financial interests.

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Introduction

Epidemiologic studies have indicated acute cardiovascular effects of fine particulate matter (PM2.5; particulate matter with aerodynamic diameter 2.5 μm) (Brook 2008; Dominici et al. 2006; Pope and Dockery 2006; Stafoggia et al. 2013). Because PM2.5 is a complex mixture of various chemical species, there is an ongoing effort to identify its health-relevant components. Nationwide multisite studies in the United States have examined whether the associations between PM2.5 and cardiovascular morbidity and mortality are modified by PM2.5 chemical composition (Bell et al. 2009; Franklin et al. 2008; Zanobetti et al. 2009). Other time-series studies have estimated associations between cardiovascular morbidity and mortality and individual PM2.5 components directly (Atkinson et al. 2015; Basagaña et al. 2015; Bell et al. 2014; Ito et&nfbsp;al. 2011; Levy et al. 2012; Lippmann et al. 2013; Ostro et al. 2006; Peng et al. 2009; Sarnat et al. 2015; Suh et al. 2011). Although the specific components that are associated with health outcomes vary across studies, there is growing evidence on the acute cardiovascular effects of metals/metalloids and carbonaceous components of PM2.5 (Kelly and Fussell 2012; Lippmann 2014; Rohr and Wyzga 2012).

Metals/metalloids exist in PM2.5 in different forms, with some forms being more water soluble and thus more biologically accessible than others (Allen et al. 2001; Birmili et al. 2006; Fang et al. 2015a; Heal et al. 2005). However, most ambient air pollution monitoring networks only measure these components in total elemental concentrations, and not in water-soluble concentrations. As a result, few epidemiologic studies have estimated health associations with PM2.5 water-soluble metals in their attempts to identify health-relevant components of PM2.5 (Heal et al. 2009; Huang et al. 2003).

To advance our understanding of acute cardiovascular effects of PM2.5 and its components, we conducted a time-series study in Atlanta, Georgia, to estimate the associations between daily counts of emergency department (ED) visits for cardiovascular diseases and daily concentrations of PM2.5 components, including a suite of PM2.5 water-soluble metals/metalloids that are not routinely measured at the ambient level. This analysis utilized up to 15 y of data on ambient air pollution and ED visits obtained as part of our ongoing Study of Particles and Health in Atlanta (SOPHIA) (Metzger et al. 2004; Sarnat et al. 2008; Tolbert et al. 2000; Ye et al. 2017).

Methods

Air Pollution Data

Ambient concentrations of criteria gases, PM2.5, and PM2.5 components were measured at the Jefferson Street ambient monitoring site during the period of 14 August 1998–15 December 2013 as part of the South Eastern Aerosol Research and Characterization (SEARCH) network and the Aerosol Research and Inhalation Epidemiology Study (ARIES) in Atlanta (Hansen et al. 2006). Criteria gases were measured daily, including 1-h maximum carbon monoxide (CO), 1-h maximum nitrogen dioxide (NO2), 1-h maximum sulfur dioxide (SO2), and 8-h maximum ozone (O3). PM2.5 and its major components—including organic carbon (OC), elemental carbon (EC), ammonium (NH4), nitrate (NO3), and sulfate (SO4)—were measured daily using filter-based 24-h integrated Federal Reference Methods. Total elemental concentrations of PM2.5 metals and metalloids (henceforth all referred to as metals), including titanium (Ti), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), aluminum (Al), lead (Pb), silicon (Si), calcium (Ca), sodium (Na), and potassium (K), were analyzed from the daily PM2.5 filters using X-ray fluorescence. X-ray fluorescence analyses were conducted by Desert Research Institute (Reno, NV) on filters collected through 22 March 2008, and by Atmospheric Research & Analysis, Inc. (Cary, NC) on filters collected after 23 March 2008; different limits of detection (LOD) were reported before and after the laboratory change for each species. Water-soluble concentrations of PM2.5 metals, including water-soluble vanadium (WS V), water-soluble chromium (WS Cr), water-soluble manganese (WS Mn), water-soluble iron (WS Fe), water-soluble nickel (WS Ni), and water-soluble copper (WS Cu), were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) during 14 August 1998–6 April 2008. Starting on 7 April 2008, these water-soluble fractions were analyzed using inductively coupled plasma mass spectrometry (ICP-MS); again, different LODs were reported before and after the analytical change for these species. Additional water-soluble species—including water-soluble zinc (WS Zn), water-soluble cadmium (WS Cd), water-soluble lead (WS Pb), water-soluble selenium (WS Se), water-soluble arsenic (WS As), water-soluble barium (WS Ba), and water-soluble lanthanum (WS La)—were reported starting on 7 April 2008 from ICP-MS analyses. All water-soluble measures were available daily before 2009 and on one-in-three days after 2009.

The LODs of all PM2.5 metals are listed in Table S1. We calculated the percentage of samples below the LOD over the full time period, and over the time periods before and after measurement/laboratory changes separately. For this analysis, we included PM2.5 metals whose concentrations were above the LOD on at least 85% of days.

Ultimately, six PM2.5 metals (Si, K, Ca, Fe, Zn, WS Fe) were included in the analysis over the full time period (14 August 1998–15 December 2013), along with criteria gases (CO, NO2, SO2, and O3), PM2.5 mass, and PM2.5 major components (OC, EC, NO3, and SO4). We did not include NH4 in epidemiologic analyses because this component mainly exists as NH4NO3 or NH4SO4 Fifteen additional PM2.5 metals were included in the analysis over the later time period (7 April 2008–15 December 2013): Al, Na, Cu, Ti, WS Cr, WS Cu, WS Mn, WS Ni, WS V, WS As, WS Ba, WS Se, WS Zn, WS Cd, and WS Pb. For species included in the analysis, any observations below the LOD were assigned a value of the LOD divided by 2.

Emergency Department Visits

We obtained daily counts of cardiovascular ED visits for patients living within the five-county Atlanta area (Clayton, Cobb, DeKalb, Fulton, and Gwinnett) during 14 August 1998–15 December 2013. Daily ED visit counts were aggregated from individual-level billing records from metropolitan Atlanta hospitals as part of SOPHIA (Metzger et al. 2004; Winquist et al. 2016). We identified cardiovascular ED visits as those billing records with primary International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes for ischemic heart disease (ICD-9 410–414), cardiac dysrhythmias (ICD-9 427), congestive heart failure (ICD-9 428), or peripheral vascular and cerebrovascular disease (ICD-9 433–437, 440, 443–445, 451–453).

Analytic Approach

In a time-series framework, we estimated the associations between daily levels of air pollutants and daily counts of cardiovascular ED visits using Poisson regression accounting for over-dispersion. Based on our previous research of ambient air pollution and cardiovascular ED visits in Atlanta, we used the same-day (lag 0) pollution level (Metzger et al. 2004; Sarnat et al. 2013; Tolbert et al. 2000; Ye et al. 2017).

All models included the same covariate control for temporal trends and meteorology: time splines with monthly knots, cubic function of same-day maximum temperature, cubic function of lag 1-2–d moving average minimum temperature, cubic function of lag 0-1-2–d moving average mean dew point temperature, day of week, indicators for holidays, seasons, season–maximum temperature interaction, season–day of week interaction, indicators for hospital participation periods, and indicator for changes in air pollution measurement. The estimated associations were reported as rate ratios (RR) with 95% confidence intervals (CI) per interquartile range (IQR) increase in pollutant concentrations.

Primary Analysis

We included criteria gases (CO, NO2, SO2, and O3), PM2.5 mass, PM2.5 major components (OC, EC, NO3, and SO4), and PM2.5 metals (Si, K, Ca, Fe, Zn, WS Fe) in the analysis over the full time period (14 August 1998–15 December 2013). Fifteen additional PM2.5 metals (Al, Na, Cu, Ti, WS Cr, WS Cu, WS Mn, WS Ni, WS V, WS As, WS Ba, WS Se, WS Zn, WS Cd, and WS Pb) were included in the analysis over the later time period (7 April 2008–15 December 2013).

We first estimated the associations between these pollutants and cardiovascular ED visits using single-pollutant models. Based on the results, we applied multipollutant models to assess copollutant confounding. Because previous studies have reported differing effects of particulate matter on cardiovascular outcomes in cold versus warm days (Ito et al. 2011; Lippmann et al. 2013), we performed analyses in the warm and cold seasons separately for pollutants available over the full time period to see if the patterns of associations across pollutants were similar. We defined the warm season as May to October and the cold season as November to April in Atlanta.

For comparability, we restricted the analyses in each time period to days on which all pollutants were available. Thus, over the full time period, year-round analyses included 3,303 d; warm-season analyses included 1,737 d; and cold-season analyses included 1,566 d. Over the later time period, year-round analyses included 628 d.

Sensitivity Analyses

We evaluated model misspecification by estimating the associations between tomorrow’s pollutant levels and today’s ED visits, controlling for today’s (lag 0) pollutant and covariate levels. Tomorrow’s pollutant levels should not be associated with today’s ED visits in the absence of confounding, measurement error, or other model misspecification, because cause must precede effect (Flanders et al. 2011). To accommodate pollutants with one-in-three–day measurements, we defined “tomorrow” as the third day after today (lag3).

We restricted the primary analysis to days on which all pollutants were available so that the health associations of different pollutants were estimated on the same set of days (n=3,303 for the 1998–2013 year-round analysis; n=628 for the 1998–2013 year-round analysis). However, this led to reduced statistical power. As a sensitivity analysis, we performed the same set of analyses without this restriction by using all available days to see if the estimated associations were similar to those in the primary analysis.

Results

We calculated descriptive statistics of the pollutants over all seasons (Table 1), in the warm season (see Table S2a), and in the cold season (see Table S2b). OC, EC, NH4, SO4, and NO3 together contributed about 80% of the PM2.5 mass, whereas the concentrations of metals were much lower. Among metals, Si and Fe were most abundant. Water-soluble Fe had the highest average concentration among water-soluble species [as commonly seen in other studies (Allen et al. 2001; Birmili et al. 2006; Duan et al. 2014; Fang et al. 2015a; Lough et al. 2005)]. Secondary pollutants such as O3 and SO4 had higher concentrations in the warm than in the cold season, whereas primary pollutant such as CO had higher concentrations in the cold than in the warm season. The concentrations of metals were generally similar in the warm and cold seasons, whereas water-soluble Fe was higher in the warm than in the cold season.

Table 1. Summary statistics of ambient air pollutants measured at the Atlanta Jefferson Street monitoring site.
Pollutants Unit n Mean±SD 50th (25th, 75th) percentiles Interquartile range
14 August 1998–15 December 2013
 Criteria gases
  CO ppm 5,458 0.86±0.83 0.56 (0.36, 1.02) 0.66
  NO2 ppb 5,321 37.2±15.2 35.9 (26.4, 46.3) 20.0
  SO2 ppb 5,465 13.4±14.7 8.1 (3.2, 18.7) 15.5
  O3 ppb 5,490 42.1±19.9 39.6 (27.2, 54.9) 27.7
PM2.5
  PM2.5 mass μg/m3 5,588 14.46±7.69 12.81 (8.93, 18.21) 9.28
  OC μg/m3 5,546 3.67±2.08 3.22 (2.31, 4.47) 2.16
  EC μg/m3 5,515 1.26±0.98 0.98 (0.63, 1.58) 0.95
  NO4 μg/m3 5,563 1.39±1.00 1.10 (0.72, 1.73) 1.01
  NO3 μg/m3 5,569 0.81±0.77 0.55 (0.31, 1.06) 0.75
  SO4 μg/m3 5,572 3.88±2.96 2.94 (1.88, 4.87) 2.99
  Si ng/m3 4,932 94.51±112.37 68.16 (39.78, 110.79) 71.01
  K ng/m3 4,932 63.78±83.88 50.80 (35.28, 75.54) 40.26
  Ca ng/m3 4,932 36.42±29.71 29.32 (18.29, 44.71) 26.41
  Fe ng/m3 4,921 76.51±59.39 60.29 (39.64, 95.10) 55.47
  Zn ng/m3 4,880 11.35±11.16 8.84 (5.73, 13.31) 7.58
  WS Fe ng/m3 4,085 24.22±20.63 18.67 (10.81, 31.28) 20.46
7 April 2008–15 December 2013
  Na ng/m3 1,930 38.86±39.29 26.03 (14.66, 47.09) 32.43
  Al ng/m3 1,931 45.96±59.49 31.75 (17.37, 56.11) 38.74
  Ti ng/m3 1,931 4.47±3.91 3.62 (2.33, 5.36) 3.03
  Cu ng/m3 1,916 5.32±10.40 3.78 (2.44, 5.68) 3.23
  WS V ng/m3 805 0.20±0.19 0.14 (0.07, 0.26) 0.19
  WS Cr ng/m3 805 0.14±0.17 0.10 (0.06, 0.15) 0.09
  WS Mn ng/m3 796 1.20±0.98 0.94 (0.57, 1.54) 0.96
  WS Ni ng/m3 805 0.30±0.68 0.15 (0.09, 0.25) 0.16
  WS Cu ng/m3 790 2.83±4.56 1.84 (1.10, 3.06) 1.96
  WS Zn ng/m3 682 8.99±6.14 7.32 (4.69, 11.16) 6.47
  WS As ng/m3 805 0.68±0.53 0.56 (0.36, 0.80) 0.44
  WS Se ng/m3 805 0.72±0.59 0.55 (0.33, 0.92) 0.59
  WS Cd ng/m3 805 0.08±0.08 0.06 (0.04, 0.09) 0.05
  WS Ba ng/m3 805 3.24±3.23 2.45 (1.36, 4.10) 2.74
  WS Pb ng/m3 803 1.39±2.98 0.87 (0.56, 1.42) 0.86

Note: Criteria gases were measured daily, including 1-h maximum carbon monoxide (CO), 1-h maximum nitrogen dioxide (NO2), 1-h maximum sulfur dioxide (SO2), and 8-h maximum ozone (O3). PM2.5 and its major components, including organic carbon (OC), elemental carbon (EC), ammonium (NH4), nitrate (NO3), and sulfate (SO4), were measured daily using filter-based 24-h integrated Federal Reference Methods. Total elemental concentrations of PM2.5 metals and metalloids, including titanium (Ti), manganese (Mn), iron (Fe), copper (Cu), zinc (Zn), aluminum (Al), lead (Pb), silicon (Si), calcium (Ca), sodium (Na), and potassium (K), were analyzed from the daily PM2.5 filters using X-ray fluorescence. Water-soluble concentrations of PM2.5 metals, including water-soluble vanadium (WS V), water-soluble chromium (WS Cr), water-soluble manganese (WS Mn), water-soluble iron (WS Fe), water-soluble nickel (WS Ni), and water-soluble copper (WS Cu), were analyzed using inductively coupled plasma optical emission spectrometry (ICP-OES) during 14 August 1998–6 April 2008 and using inductively coupled plasma mass spectrometry (ICP-MS) starting on 7 April 2008. Water-soluble zinc (WS Zn), water-soluble cadmium (WS Cd), water-soluble lead (WS Pb), water-soluble selenium (WS Se), water-soluble arsenic (WS As), water-soluble barium (WS Ba), and water-soluble lanthanum (WS La) were reported starting on 7 April 2008 from ICP-MS analyses. All water-soluble measures were available daily before 2009 and one-in-three days after 2009.

Pearson correlations of the pollutants were also calculated over all seasons (see Table S3), in the warm season (see Table S4a), and in the cold season (see Table S4b). Over all seasons, PM2.5 was most correlated with SO4 (r=0.80), OC (r=0.74), EC (r=0.67), and WS Fe (r=0.65). Water-soluble Fe was most correlated with SO4 (r=0.61) and Fe (r=0.64). OC and EC were highly correlated with one another (r=0.79), and their correlations with other PM2.5 components were weak to moderate (r=0.170.58). PM2.5 was more strongly correlated with SO4 and O3 in the warm season, and with EC, OC, and metals in the cold season.

Summary statistics of cardiovascular ED visits are listed in Table 2. Briefly, average daily counts of cardiovascular ED visits were 76 during the full time period and 96 during the later time period. The average daily counts were similar in the warm and cold seasons in Atlanta.

Table 2. Summary statistics of emergency department visits for cardiovascular diseases.
Time period Total visits (n) Average visits [n (SD)] Min, max visits (n, n)
14 August 1998–15 December 2013 Year-round 426,252 76 (22) (21, 143)
Warm season 210,020 74 (21) (23, 136)
Cold Season 216,232 78 (23) (21, 143)
7 April 2008–15 December 2013 Year-round 199,343 96 (14) (55, 143)
Warm season 102,793 93 (13) (61, 136)
Cold season 96,550 99 (14) (55, 143)

Note: Daily counts of emergency department visits were aggregated from individual-level billing records from metropolitan Atlanta hospitals for patients living within the five-county Atlanta area (Clayton, Cobb, DeKalb, Fulton, and Gwinnett). We identified emergency department visits for cardiovascular diseases as those billing records with primary International Classification of Diseases, Ninth Revision (ICD-9) diagnosis codes for ischemic heart disease (ICD-9 410–414), cardiac dysrhythmias (ICD-9 427), congestive heart failure (ICD-9 428), or peripheral vascular and cerebrovascular disease (ICD-9 433–437, 440, 443–445, 451–453). Warm season includes May to October, and cold season includes November to April. Max, maximum; min, minimum.

Primary Analysis

We estimated the associations between cardiovascular ED visits and pollutants available during the full time period using single-pollutant models. The estimated RRs were positive for a number of pollutants, including criteria gases, PM2.5 mass, and PM2.5 components (OC, EC, NO3, Si, Ca, Fe, Zn, water-soluble Fe) (Figure 1). Among them, the estimated RR per IQR increase in water-soluble Fe was the highest [RR=1.012 (95% CI: 1.005, 1.019)].

Figures 1a and 1b are plots showing RR (95 percent confidence intervals) per IQR increase (y-axis) across the following single-pollutant models and two-pollutant models, respectively (x-axis): C O, N O 2, S O 2, O3, PM 2.5, OC, EC, N O 3, S O 4, Si, K, Ca, Fe, Zn, and WS Fe

Figure 1. Estimated associations between cardiovascular emergency department visits and pollutants available during 1998–2013, year-round analysis (3,303 d), Atlanta, Georgia. Results from single-pollutant models (a); results from two-pollutant models: water-soluble Fe controlling for each of the other pollutants (b). Note: Ca, calcium; CO, carbon monoxide; EC, elemental carbon; Fe, iron; IQR, interquartile range; K, potassium; NO2, nitrogen dioxide; NO3, nitrate; O3, ozone; OC, organic carbon; PM2.5, particulate matter with aerodynamic diameter 2.5 μm; RR, rate ratio; Si, silicon; SO2, sulfur dioxide; SO4, sulfate; WS, water soluble; Zn, zinc.

To assess whether the association for water-soluble Fe was confounded by other pollutants, we estimated the associations between cardiovascular ED visits and water-soluble Fe controlling for each of the other measured pollutants in two-pollutant models. The associations for water-soluble Fe changed little when controlling for any of the pollutants. In contrast, the associations for PM2.5 mass and PM2.5 components (OC, EC, NO3, Si, Ca, Fe, Zn) were weaker and consistent with the null when controlling for water-soluble Fe (Figure 1).

We performed analyses in the warm (May–October) and cold (November–April) seasons separately to see if the patterns of associations were similar. In the warm season, the estimated RR per IQR increase in water-soluble Fe was the highest. The associations for PM2.5 and a number of PM2.5 components (OC, EC, SO4, K) were consistent with the null (Figure 2). Although the estimated RRs for CO, Si, Ca, Fe, and Zn were positive in single-pollutant models, they were lower in two-pollutant models with water-soluble Fe (Figure 2).

Figures 2a and 2b are plots showing RR (95 percent confidence intervals) per IQR increase (y-axis) across the following single-pollutant models and two-pollutant models, respectively (x-axis): C O, N O 2, S O 2, O3, PM 2.5, OC, EC, N O 3, S O 4, Si, K, Ca, Fe, Zn, and WS Fe

Figure 2. Estimated associations between cardiovascular emergency department visits and pollutants available during 1998–2013, warm-season analysis (1,737 d). Results from single-pollutant models (a); results from two-pollutant models: water-soluble Fe controlling for each of the other pollutants (b). Note: Ca, calcium; CO, carbon monoxide; EC, elemental carbon; Fe, iron; IQR, interquartile range; K, potassium; NO2, nitrogen dioxide; NO3, nitrate; O3, ozone; OC, organic carbon; PM2.5, particulate matter with aerodynamic diameter 2.5 μm; RR, rate ratio; Si, silicon; SO2, sulfur dioxide; SO4, sulfate; WS, water soluble; Zn, zinc.

In the cold season, the estimated associations across pollutants were generally higher than those in the warm season (Figures 2 and 3). Among PM2.5 components, the estimated RR for water-soluble Fe was still the highest (Figure 3). The associations for CO, PM2.5, OC, EC, NO3, SO4, Si, K, and Ca were weaker and consistent with the null when controlling for water-soluble Fe. The association for water-soluble Fe was weaker in two-pollutant models with Fe (Figure 3).

Figures 3a and 3b are plots showing RR (95 percent confidence intervals) per IQR increase (y-axis) across the following single-pollutant models and two-pollutant models, respectively (x-axis): C O, N O 2, S O 2, O3, PM 2.5, OC, EC, N O 3, S O 4, Si, K, Ca, Fe, Zn, and WS Fe

Figure 3. Estimated associations between cardiovascular emergency department visits and pollutants available during 1998–2013, cold-season analysis (1,566 d). Results from single-pollutant models (a); results from two-pollutant models: water-soluble Fe controlling for each of the other pollutants (b). Note: Ca, calcium; CO, carbon monoxide; EC, elemental carbon; Fe, iron; IQR, interquartile range; K, potassium; NO2, nitrogen dioxide; NO3, nitrate; O3, ozone; OC, organic carbon; PM2.5, particulate matter with aerodynamic diameter 2.5 μm; RR, rate ratio; Si, silicon; SO2, sulfur dioxide; SO4, sulfate; WS, water soluble; Zn, zinc.

Measurements of an additional 15 PM2.5 metals were only available during the later time period. We estimated their associations with cardiovascular ED visits using single-pollutant models. The estimated RRs were the highest for water-soluble V [RR=1.012 (95% CI: 1.000, 1.025)] and Na [RR=1.008 (95% CI: 0.998, 1.017)]. We also estimated the association for water-soluble Fe in this later time period. The estimated RR (95% CI) for water-soluble Fe was 1.014 (0.988, 1.041) in the single-pollutant model, which was similar to that during the full time period, and the estimated RR had little change when controlling for any of the other metals in two-pollutant models (Figure 4).

Figures 4a and 4b are plots showing RR (95 percent confidence intervals) per IQR increase (y-axis) across the following single-pollutant models and two-pollutant models, respectively (x-axis): WS Fe, Na, Al, Ti, Cu, WS V, WS Cr, WS Mn, WS Ni, WS Cu, WS Zn, WS As, WS Cd, WS Ba, and WS Pb

Figure 4. Estimated associations between cardiovascular emergency department visits and pollutants only available during 2008–2013 and water-soluble Fe during 2008–2013, year-round analysis (628 d). Results from single-pollutant models (a); results from two-pollutant models: water-soluble Fe controlling for each of the other pollutants (b). Note: Al, aluminum; Cu, copper; IQR, interquartile range; Na, water-soluble sodium; RR, rate ratio; Ti, titanium; WS, water soluble; WS As, water-soluble arsenic; WS Ba, water-soluble barium; WS Cd, water-soluble cadmium; WS Cr, water-soluble chromium; WS Cu, water-soluble copper; WS Fe, water-soluble iron; WS Mn, water-soluble manganese; WS Pb, water-soluble lead; WS V, water-soluble vanadium; WS Zn, water-soluble zinc.

Sensitivity Analyses

For single-pollutant models in the year-round analysis, we evaluated model misspecification by estimating the associations between tomorrow’s pollutant levels and today’s ED visits, controlling for today’s pollutant and covariate levels. We found associations between cardiovascular ED visits and tomorrow’s levels of WS Ni and WS Mn, suggesting possible model misspecification when estimating these associations (see Figures S1 and S2). All other associations with tomorrow’s pollutant levels were consistent with the null, as expected under a well-specified model.

We restricted the primary analysis to days on which all pollutants were available. However, this led to reduced statistical power. We performed the same set of analyses without this restriction as a sensitivity analysis. We observed patterns of associations similar to those in the primary analysis, except that the association for SO4 in the cold season was more positive and the association for NO3 was more negative in this sensitivity analysis than in the primary analysis (see Figures S3–S6).

Discussion

In this study, we estimated acute cardiovascular effects of PM2.5 and its components, including a suite of water-soluble metals that are not routinely measured at the ambient level. We performed two-pollutant analysis to account for copollutant confounding, and compared the patterns of associations across pollutants in the warm and cold seasons.

Among the PM2.5 components we examined during the full time period (1998–2013), water-soluble Fe had the strongest estimated effect in both the warm and cold seasons. The associations for PM2.5 and other PM2.5 components were generally weak and consistent with the null when controlling for water-soluble Fe. Among PM2.5 components that were only measured during the later time period (2008–2013), water-soluble V was associated with cardiovascular ED visits.

Oxidative stress has been suggested as a central mechanism by which particulate matter affect health (Ghio et al. 2012). Transition metals can generate reactive oxygen species (ROS) in living systems, leading to oxidative stress (Ghio et al. 2012; Stohs and Bagchi 1995). Redox-active transition metals—such as Fe, Cu, Mn, and V—can act as catalysts of Fenton or Fenton-like reactions, facilitating the conversion of superoxide anion and hydrogen peroxide to hydroxyl radical (Chevion 1988; Stohs and Bagchi 1995). Because particle-bound metals need to dissolve and become metal ions to participate in these reactions, the water-soluble fractions of metals are thought to be more biologically relevant than total metals (Birmili et al. 2006; Urch et al. 2004). Recent studies have used cellular and cell-free assays to measure the oxidative potential of ambient particulate matter and have suggested that water-soluble metals—especially water-soluble Fe, water-soluble Cu, and water-soluble Mn—contribute to the ROS generation of particulate matter (Abrams et al. 2017; Cheung et al. 2012; Fang et al. 2015b; Landreman et al. 2008; Shen and Anastasio 2011; Verma et al. 2010). In our analysis, however, we observed positive associations with water-soluble Fe, but not with water-soluble Cu or water-soluble Mn. One reason could be that these species are less abundant than water-soluble Fe in the ambient air and thus could be more subject to measurement error, resulting in more underestimated health associations.

The observed associations with water-soluble Fe could indicate cardiovascular effects of certain pollution mixtures. Metals are released to the atmosphere from various sources, including natural processes acting on crustal minerals, resuspension of road dust and brake/tire wear abrasion during traffic, combustion of fossil fuels and wood, industrial processes, and waste incineration (Allen et al. 2001; Birmili et al. 2006; Duan et al. 2014; Fang et al. 2015a; Grigoratos and Martini 2015; Ito et al. 2004; Lin et al. 2015; Seinfeld 2006). Crustal species such as silicon, iron, calcium, sodium, aluminum, and potassium are largely found in the resuspension of road dust; meanwhile, copper, barium, manganese, iron, zinc, and chromium are commonly related to brake/tire wear debris; Nickel and vanadium are often attributed to residual oil combustion (Allen et al. 2001; Birmili et al. 2006; Duan et al. 2014; Fang et al. 2015a; Grigoratos and Martini 2015; Ito et al. 2004; Lin et al. 2015; Seinfeld 2006). The water-soluble fractions of these metals are partly from direct emission and partly from secondary processing of the primary insoluble metals by acid dissolution. A recent study in Atlanta investigated source contributions of a suite of water-soluble metals (Fang et al. 2015a). Roadway emissions, such as brake/tire wear debris and the resuspension of road dust followed by secondary processing by acid, were suggested as major contributors of a number of water-soluble metals, including water-soluble Fe, water-soluble Cu, water-soluble Mn, and water-soluble Zn. For water-soluble Fe, source apportionment attributed over 30% to mechanical abrasion of automobile brakes/tires and another 50% to secondary processing of Fe by acid (Fang et al. 2015a). Acid dissolution has been suggested as a major source of water-soluble Fe and other transition-metal ions in recent studies. Size distributions of soluble metals and particle pH have shown that sulfate plays a key role in producing highly acidic fine particles that are capable of dissolving primary transition metals (Fang et al. 2017). Single-particle analysis has shown that the majority of water-soluble Fe in Atlanta is in the form of iron sulfate (Longo et al. 2016; Oakes et al. 2012). We also found in this study that water-soluble Fe is mostly correlated with total Fe (r=0.64) and SO4 (r=0.61). These observations are consistent with the proposed mechanism of metal dissolution by acidic sulfate. The association we observed with water-soluble Fe points to certain aspects of roadway emission, when processed by acidic sulfate, as a mixture harmful for cardiovascular health. In our analysis, however, associations with other roadway-related metal species, such as water-soluble Cu, water-soluble Mn, water-soluble Zn, and water-soluble Ba, were consistent with the null. Again, these species are less abundant than water-soluble Fe in the ambient air and thus could be more subject to measurement error, resulting in more underestimated health associations.

The co-influence of the two sources on the levels of water-soluble Fe—primary roadway emission and secondary processing of the roadway emission by acidic sulfate—is reflected in the temporal trends of total Fe, water-soluble Fe, and sulfate in our study (Figure 5). Fe (i.e., total Fe) has no discernable seasonal or long-term trend. In contrast, the temporal trend of water-soluble Fe follows that of the sulfate: sulfate has a distinct seasonal trend with peaks in summer and a long-term decreasing trend potentially due to SO2 controls on coal-fired electrical generating units as well as their replacements (i.e., natural gas-fired units) (de Gouw et al. 2014). These observed trends illustrate how complex interactions between differing pollutant sources could affect the levels of potentially harmful components in PM2.5 and a co-benefit of SO2 reduction.

Three graphical representations respectively plotting concentrations of Fe in nanograms per cubic meter, water-soluble Fe in nanograms per cubic meter, and sulfate measured in micrograms per cubic meter (y-axis) across years 1998 to 2013 (x-axis).

Figure 5. Daily concentrations of Fe, water-soluble Fe, and sulfate measured at the Atlanta Jefferson Street ambient monitor, 14 August 1998–15 December 2013. Note: Fe (i.e., total Fe) was analyzed from daily PM2.5 filters using X-ray fluorescence. Water-soluble Fe was analyzed using ICP-OES during 14 August 1998–6 April 2008 and using ICP-MS starting on 7 April 2008. Measurements of water-soluble Fe was daily before 2009 and one-in-three days after 2009. Sulfate was measured daily using filter-based 24-h integrated Federal Reference Methods. Fe, iron; ICP-MS, inductively coupled plasma mass spectrometry; ICP-OES, inductively coupled plasma optical emission spectrometry; PM2.5, particulate matter with aerodynamic diameter 2.5 μm.

Fe (i.e., total Fe) and water-soluble Fe were both included in our analysis over the full time period, and their associations with cardiovascular ED visits were similar in single-pollutant models. In the warm season, the association with total Fe was consistent with the null when controlling for water-soluble Fe, suggesting that the water-soluble fraction was driving the association of Fe. This is expected if iron is a causal agent and its water-soluble fraction is more biologically accessible. However, we did not observe this pattern of associations in the cold season. One reason could be that the concentrations of water-soluble Fe in the cold seasons were much lower than in the warm seasons (see Table S2), and thus could be more subject to measurement error compared with total Fe, whose concentrations were similar in cold and warm seasons.

In fact, in the cold season, other PM2.5 components, such as EC and OC had stronger associations with cardiovascular ED visits than in the warm season. Although the associations for EC and OC were weaker when controlling for water-soluble Fe, the association of water-soluble Fe was also slightly weaker in two-pollutant models with these pollutants. EC and OC are partly from tailpipe emissions, and together with roadway-related species such as total Fe and water-soluble Fe, these pollutants may all contribute to cardiovascular effects of traffic pollution in the cold season.

Epidemiologic evidence on cardiovascular effects of water-soluble metals is sparse. In a time-series study in Edinburgh, Scotland, Heal et al. (2009) estimated the associations between cardiovascular hospital admissions and a number of PM2.5 total and water-soluble metals, including Cu, Fe, Ni, V, and Zn. However, direct measurements of these species were only available for 1 y, during which they did not find significant associations with total or water-soluble metals, nor with PM2.5 mass. Huang et al. (2003) exposed a panel of 38 healthy adults to concentrated ambient particles (CAP) from Chapel Hill, North Carolina, and reported that water-soluble metals in CAP (the V/Cu/Zn factor by principal component analysis) was associated with increased blood fibrinogen levels.

A number of studies have provided general evidence for acute cardiovascular effects of PM2.5 metals, although they only considered total elemental concentrations, not water-soluble fractions of metals (Basagaña et al. 2015; Bell et al. 2014; Bilenko et al. 2015; Huang et al. 2003; Ito et al. 2011; Lippmann et al. 2013; Morishita et al. 2015; Ostro et al. 2007; Suh et al. 2011; Urch et al. 2004; Zhang et al. 2016; Zhou et al. 2011). Suh et al. (2011) combined Cu, Mn, Zn, Ti, and Fe in a transition-metal category and reported positive associations with cardiovascular hospital admissions in a time-series study in Atlanta. In a time-series study in New York City, Ito et al. (2011) reported positive associations between cardiovascular hospital admissions and a number of PM2.5 components (OC, EC, SO4, Ni, V, Zn, Se, Br). Similarly in a time-series study of 64 U.S. counties, Lippmann et al. (2013) found positive associations between cardiovascular hospital admissions and OC, EC, SO4, Fe, V, and Zn. Zhang et al. (2016) reported that short-term exposures to transition metals (Cr, Fe, Cu, Mn, and Ni) in the ambient air were associated with decreased microvascular function in a panel of adults in Los Angeles, California. Morishita et al. (2015) found that a number of PM2.5 metals (As, Ca, Ce, Fe, Mg, Mn, S, Se, Ti) were associated with heart rate in a panel of adults in Dearborn, Michigan.

Some studies reported stronger associations with carbonaceous components than metals (Bell et al. 2014; Sarnat et al. 2015). In a time-series study in the St. Louis, Missouri–Illinois, area, Sarnat et al. (2015) found positive associations between cardiovascular ED visits and carbonaceous constituents (OC, EC, and certain hopanes), but not with metals (Si, K, Ca, Fe, Cu, Zn, and Pb). Bell et al. (2014), in a time-series study in four New England counties, observed positive associations between cardiovascular hospital admissions and black carbon, Ca, Zn, and V, where the association with black carbon was stronger than with the metals and was robust to copollutant adjustment of these metals. The inconsistencies between our study and these previous studies may be due to a number of factors, including the specific components being examined, copollutant confounding, pollutant interactions, nonlinear dose response, differences in population susceptibility, and measurement error. In particular, these studies considered only total elemental, not water-soluble, metals; besides, given that OC is itself a mixture of organic compounds, its health effects also depend on its composition, which likely varies by study location. In addition, previous studies have suggested synergism between organic compounds and metals in generating reactive oxygen species (Ghio et al. 2012; Li et al. 2009). Health associations of organic pollutants could depend on the levels of metals, and vice versa, which further complicates the comparison of health effects across PM components.

There are several limitations to our study. Our results are subject to spatial misalignment and instrument measurement error, and the degree of these sources of error likely differs by pollutant. Compared with pollutants dominated by secondary origins (e.g., O3, PM2.5, NO3, SO4, water-soluble metals), primary pollutants (e.g., EC, Fe, Cu, Zn) are likely more subject to spatial misalignment due to their greater spatiotemporal heterogeneity, and thus their estimated associations may be more biased towards the null. Additionally, pollutants with a lower ambient concentration may be more subject to instrument measurement error, leading to an underestimation of effects.

Conclusions

Our study suggests cardiovascular effects of certain water-soluble metals, particularly water-soluble Fe, which has not been well studied previously. Our findings further elucidate the link between traffic emissions, atmospheric secondary processing, and cardiovascular health, and contribute to the ongoing effort to identify causal mixtures in air pollution. The co-influence of two sources on the levels of water-soluble metals, roadway emission and secondary processing of the roadway emission by acidic sulfate, has implications for pollution control strategies.

Acknowledgments

The authors acknowledge the contributions of members of the Southeastern Center for Air Pollution and Epidemiology (SCAPE) research group. This publication is based in part upon information obtained from the Georgia Hospital Association and individual hospitals; we are grateful for the support of all participating hospitals.

Research reported in this publication was supported by funding from the Electric Power Research Institute (EPRI, 10002467). This publication was also made possible by a Clean Air Research Center grant to Emory University and the Georgia Institute of Technology from the U.S. Environmental Protection Agency (EPA, RD834799), as well as by grants to Emory University from the U.S. EPA (R82921301), the National Institute of Environmental Health Sciences (R01ES11294), and the EPRI (EP-P27723/C13172 and EP-P4353/C2124).

The content of this publication is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the U.S. EPA. Further, the U.S. EPA does not endorse the purchase of any commercial products or services mentioned in the publication.

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Does the study fit? Matching economic analyses to coastal management questions and communication needs

In managing U.S. estuaries, as well as throughout ocean and coastal governance, there is an increasing call for economic research to inform decisions and to communicate the values of environmental resources to local communities, policy makers, and other stakeholders. Watershed managers implement economic studies to: 1) better communicate the value of estuarine resources to the wider community, 2) determine the most cost-effective management actions, and 3) compare the costs and benefits of actions to improve water quality. In order to better understand how economic studies are applied and their usefulness in coastal management, we interviewed managers from six National Estuary Programs (NEPs) and two watershed organizations that have commissioned economic studies. We also reviewed the analyses performed in the studies, focusing on the lessons learned from the use of those studies for coastal management and how well the studies matched the stated needs and desires of the managers. We found there is often a disconnect between what managers said they wanted from the economic analyses and what they actually got. Yet, almost universally, the managers found their studies to be useful tools for communication and raising awareness. In this presentation, we discuss key takeaways from our interviews with the coastal watershed managers. We make suggestions for how managers undertaking studies in the future might better articulate their needs to determine the most appropriate economic approaches, and avoid some of the pitfalls faced by other managers in conducting and communicating economic analyses. Additionally, our findings may help economists understand the needs of estuary managers, and help them better provide economic research that can contribute effectively to coastal management.