Department Press Briefings : Department Press Briefing – August 18, 2017

Heather Nauert


Department Press Briefing

Washington, DC

August 18, 2017

Index for Today’s Briefing



    2:21 p.m. EDT

    MS NAUERT: Hi, how are you? Hi, everybody. How is everyone today?

    QUESTION: Friday.

    QUESTION: Tired.

    MS NAUERT: I know, Friday – ready for the week to be over, right? Okay. But it has been a busy couple days, certainly.

    Let me start out by first addressing what happened in Spain yesterday and overnight. The United States wants to strongly condemn the terror attack that took place in Barcelona, Spain. We extend our condolences to the family and loved ones of the victims and the people of Spain, as well as our hopes for a quick recovery for those who have been wounded. The United States stands in solidarity with Spain. Crimes like this cowardly attack only reinforce our shared resolve to stop these senseless attacks that target the innocent.

    The U.S. consulate general in Barcelona continues to work with local authorities to identify and provide assistance to U.S. citizens affected by the terror attacks in Las Ramblas and in Cambrils. As Secretary Tillerson said earlier today, we can confirm that one American citizen was killed in that attack, and our thoughts and prayers go out to his family and his friends. We can also confirm that there was a injury of another U.S. citizen. It was a minor injury, we’re told. Out of respect for the family’s privacy and in their time of grief, we have no further comment on that matter.

    Spanish authorities report that there are still several casualties who have not yet been identified. The U.S. consulate in Barcelona continues to issue emergency and security messages to update U.S. citizens in the area. U.S. citizens are advised to maintain security awareness and monitor media and local information sources. We also strongly encourage U.S. citizens in Barcelona to contact their family and friends back here in the United States to directly inform them of their safety and their security. President Trump spoke with President Rajoy today to – and said to him that we stand ready to offer any assistance necessary to Spanish authorities as they pursue their investigation.

    As a second matter today, I’d like to bring this up. It’s something that takes place tomorrow, actually, and that is World Humanitarian Day. It is a time to protect aid – or recognize, rather, aid workers who have lost their lives to protect the world’s most vulnerable people. We come together as an international community on August the 19th to honor the brave men and women who heroically risk everything to serve those who are in need around the world. Nearly 300 aid workers worldwide were killed, injured, or kidnapped in 2016 alone, a particularly dangerous year for humanitarian staff. Providing humanitarian assistance and saving lives is growing harder as crises and conflicts grow in complexity and also strain scarce resources.

    Violations of international law put aid workers in grave danger. The numbers tell a pretty tough story. An unprecedented number, 141.1 million people across 37 countries, are now in immediate need of assistance. Just this week the United Nations confirmed that the number of South Sudanese refugees in Uganda has topped 1 million people as the conflict in South Sudan has created the world’s fastest-growing refugee crisis. The United States has a long and distinguished history of helping people in need as a result of conflict and natural disasters. The United States and our humanitarian partners are responding to crises around the world, providing life-saving assistance to some of the world’s most vulnerable citizens. In 2016 the United States, the world’s leading humanitarian donor, contributed more than $7 billion to humanitarian efforts around the globe. This World Humanitarian Day we remain committed to saving lives and recognize the tremendous service of all humanitarian heroes, including our brave aid workers and partners on the ground. And we want to thank them for their bravery and their work.

    With that, I will take your questions.

    QUESTION: Just very quickly —

    MS NAUERT: Hi, Matt.

    QUESTION: — on Barcelona —

    MS NAUERT: Yes.

    QUESTION: — before I get to something – can you at least – I realize you can’t give details about the two casualties, but were they killed in – were they in Barcelona or in the other place?

    MS NAUERT: They were in Barcelona.

    QUESTION: Okay, and in light —

    MS NAUERT: The one American who was killed was in Barcelona.

    QUESTION: Do you know about the injured?

    MS NAUERT: The other injury – I believe the other injury was in Barcelona as well. I can double check that for you.

    QUESTION: Okay. And was the injured person you referred to – his family, for the person who died, it – can you be more specific about the sex of the injury? Man or woman?

    MS NAUERT: Two males. Two men.

    QUESTION: Two men. Okay. All right. And then I just wanted to go to – my understanding is that your email system is back up. Is that correct?

    MS NAUERT: Temporarily back up. So our email system – so if any of you had emailed us this morning and did not get a response, that was not intentional. Our email system has been down since —

    QUESTION: Not necessarily intentionally.

    MS NAUERT: (Laughter.) I would never not get back to you all.

    QUESTION: (Off-mike.)

    MS NAUERT: (Laughter.) She says speak for yourself. Carol texted me. (Laughter.) Nevertheless, it has been quite a headache today. Our email system has been down. It was brought up just a short while ago. I understand they’re still working through some of the details. It’s something that was a technical glitch – that’s how our folks are describing it, right?

    STAFF: Internal issue.

    MS NAUERT: We literally got off the phone with them 20 seconds ago. A little longer than that. And so it was – what was – remind me.

    STAFF: Internal issue.

    MS NAUERT: It was just an internal issue, so if there’s anything different on that, we’ll bring that to you.

    QUESTION: Well, when you say “internal issue,” can you – can – you can rule out that this was, like, kind of a sabotage or an outside hacker? Because – I just remind you, this was well before your time, but in 2014 we had this issue, and we were basically given false information that this was – that the system was shut down for routine maintenance, when, in fact, it was shut down so that the technicians could go in and do battle with hackers who had infiltrated it. So you’re assuring us that there’s nothing like that?

    MS NAUERT: To my awareness, there’s nothing – that is not the case.

    QUESTION: And it’s just the unclassified system?

    MS NAUERT: Yeah, thank you. Unclassified system.

    QUESTION: Okay. But – so when you say temporarily back up, you don’t expect it to go down again, do you?

    MS NAUERT: I would hope not. There are some glitches that they’re still working out. I got a big batch of emails in about 10 minutes ago, and then didn’t. So we’re kind of sharing with you how the sausage is being made right now. (Laughter.)

    QUESTION: So it’s like everyone else’s email system – it goes up, and then goes – when it goes down, it comes back sporadically.

    MS NAUERT: I think so.

    QUESTION: Okay. That’s all I have.

    MS NAUERT: Okay.

    QUESTION: I mean, I have other stuff, but other people —

    MS NAUERT: Okay, okay. Would anyone like to talk about email or Spain? Let’s try to stick to a more organized system of regions today.

    QUESTION: Spanish email.

    MS NAUERT: Anything else on Spain? Yeah, hi.

    QUESTION: Just generally, Heather, on Spain, is the fact that an American was killed, does that change the U.S. involvement in the investigation at all, or the U.S. response at all?

    MS NAUERT: Well, we have a very close partnership and collaboration with the Spanish authorities and with the Spanish Government. The President just talked to their president a short while ago. Secretary Tillerson spoke to this yesterday, as did Mike – Vice President Mike Pence. Among the things that we have said to the Spanish Government is that we are standing by and willing to offer any assistance that they might need in the investigation or with resources in terms of helping out their folks on the ground there. That hasn’t changed; we still stand by that, and are willing – the entire body of the U.S. Government – willing to stand by to help the Spanish.

    QUESTION: Actually, on Spain, do you – I mean, apparently it was a much more complex attack and a more dangerous attack was planned using butane explosives or something like that, and then there was a couple of weeks ago a plot in Australia that the Australian authorities disrupted, and they said it was very sophisticated and was supposed to involve some sort of chemical agent. Do you see that ISIS is stepping up its attacks as it’s losing territory?

    MS NAUERT: You raise a good point about how ISIS is losing territory. And we know that coalition partners, backed by the United States in Iraq and also Syria, have taken back much, much of that territory that ISIS held in the first place. As that continues to happen, as they lose ground – they’ve lost like 70 percent of the ground that they had initially taken in Iraq, more than 50 percent of the ground that they had initially taken in Syria – they become more desperate. We do know that other European attacks that happened in the last year were plotted out of Raqqa, Syria. That is one of the reasons that the coalition has focused so much on the city of Raqqa and taking back Raqqa from ISIS, because some of those plots were hatched from Raqqa. We know that as a fact.

    What has happened now may just be an instance where they are trying to show that they may still hold some relevancy as we continue to take back ground from them.

    QUESTION: So you think it’s too early to say there’s any pattern of escalating attacks?

    MS NAUERT: I can’t say – I can’t say that. I don’t want to draw any conclusions. Spanish authorities are investigating that; I don’t want to get ahead of any of their investigations.

    Anything else on Spain? Okay, let’s move on to something else. Go right ahead, Rich.

    QUESTION: Thanks, Heather. On today’s announcement on diversity, and the Secretary’s comments on race relations in the country. It seems obvious, but just to ask: How much did Charlottesville play into the timing and the content of the Secretary’s remarks, and the announcement for this new diversity initiative? And how long has he been constructing this or thinking about it?

    MS NAUERT: Sure. So let me take you back quite a few months. The Secretary’s first day on the job, when he came in here and he went into the main hallway at the front where all the flags are at the State Department, he looked out across the crowd, and one of the things that he said to our employees is, “When people see you, they see America.” Meaning, looking at the minorities, looking at all the different faces, the different types of names and everything – that is America, and that’s what we represent, not just here in America but also overseas. And that’s a priority for him.

    Let me take you to about two weeks ago, and that’s when Deputy Secretary Sullivan spoke at our town hall meeting. One of the things that he said – it was closed press, but one of the things that he did share with the people at our town hall meeting, and who were also watching overseas who work at the State Department, was we have a commitment to diversity, and we can do a whole lot better than we currently do as a State Department.

    And so that was really the genesis of the Secretary’s comments today, in bringing in some of our interns and our – those who are involved in our fellowship programs here, Pickering and Rangel – we’ve talked about that program that intends to bring in diverse applicants into our Foreign Service program. So that’s one of the things that the Secretary focused on today, bringing them all in and addressing the issue of diversity.

    This also takes place as we undergo the redesign of the State Department, and in undergoing the redesign of the State Department, this is something that we’ll consider. We look at our overall mission and we look at our overall objectives and the scope of what we do, and this is one way to reflect on that. So the Secretary is making this a big priority of his.

    QUESTION: But certainly, he was aware of the timing of this just a few days after Charlottesville?

    MS NAUERT: Oh, and I think one would be remiss if they didn’t touch on what had happened in Charlottesville over this past week. And that’s a good reminder for all of us, not just here but Americans serving abroad, that what happened last week in Charlottesville is not representative of America. Yes, we have freedom of speech. Yes, that is something that we embrace. Hatred is not something we embrace. It’s not who we are as a people. That’s not what we want to show overseas. But it reminds us that there is still a battle that can go on internally within our own country, and it’s something that we’re working to address and to try to fix.

    QUESTION: Can I —

    QUESTION: So what’s —

    QUESTION: Can I follow up on that?

    MS NAUERT: Yeah, sure. Hey, Elise.

    QUESTION: Hi. Well, it seemed as if it was a not-so-subtle repudiation of the President’s declaration that both sides were to blame, and kind of equating the hate speech protesters and those that were protesting the statue with the peaceful protesters. And when he also brought up – when he invoked George Washington at a synagogue, kind of indirect – antithetical to President Trump’s remarks that George Washington was no different than Robert E. Lee.

    MS NAUERT: I think what the Secretary was stating is what we all think about America and what we represent as Americans, and those are the best ideals. And we represent diversity as Americans. We represent hope. The Secretary talked about this today, where we’re the kind of country where it doesn’t matter where you came from, it doesn’t matter what your parents did, it doesn’t matter what your last name is, that you too can succeed. And I think he’s hoping to not just underscore those ideals but to help promote them across the country and across the world as well.

    QUESTION: Well, would it be wrong of us to infer from his remarks that he does not believe that both sides were to blame for last week’s incidents?

    MS NAUERT: I have not asked him that question, but I think he was very clear, and I will restate some of this for you. Those who embrace poison in our public discourse, they damage the very country that they claim to love. We condemn racism. We condemn bigotry in all of its forms. Racism is evil. It is antithetical to American values. It’s antithetical to the American idea. So I think the Secretary was clear in his personal beliefs about that.

    QUESTION: On this?

    MS NAUERT: Yes.

    QUESTION: He mentioned that you would be keeping in place the Pickering and Rangel fellowship programs, which we – you had said before. I know that. But he said – he said all fellowship programs. Does that include the Presidential Management Fellows?

    MS NAUERT: I believe so. Let me double-check that part of it for you, though.

    QUESTION: So when exactly is the – I mean, the hiring freeze, with the certain exceptions that have been made already for the two A-100 classes, is in – is still in place, correct?

    MS NAUERT: Yes. So the – there’s a department-wide hiring freeze. The Secretary touched on that this morning. That hiring freeze was put in place earlier this year so we could kind of get a better temporary – it was a temporary hiring freeze.

    QUESTION: So —

    MS NAUERT: But to get a better sense as to who we have here, what our folks are doing, and what current jobs are open and what current jobs are perhaps duplicative.

    QUESTION: Right. So it is still not being lifted and it won’t be lifted until after the reorganization is complete?

    MS NAUERT: I’m not certain about the time in which it will be lifted. All I can tell you is that it’s temporary. I think that’s something that’s still under consideration.

    QUESTION: Okay. Because there’s a lot of angst and stress among this building and among former officials who think – or who have been under the impression that these programs are going away and that the Secretary was not committed to having a full – a full and effective complement of diplomats in the Foreign Service. Is that incorrect?

    MS NAUERT: Well, here’s what I can tell you. The Pickering and Rangel fellows program is staying. We have a new class that’s incoming. I talked with some of the fellows this afternoon and asked them what they thought about the speech and asked them how they’re enjoying the program, and they gave it all a thumbs up. So I know that they’re pleased with it. Of course they’re very happy that the program is remaining and we are as well, and talking with a lot of Foreign Service officers in the building, even the white guys, they all said, “We love this. We love this program. We’re so pleased that it’s staying.” So I think building-wide I can speak for that – the importance of diversity, and kidding aside. But the importance of diversity to the programs here.

    QUESTION: Well, so do you have any idea how quickly the Secretary envisions building the Foreign Service up to a point where it does reflect the face of America or it does reflect the diversity of America?

    MS NAUERT: So part of the program here – and this is something that he kind of outlined in broad brush strokes earlier today – to build a recruiting team, to go out to some schools in different places around the country so that people don’t necessarily have to seek us out – and I’m not talking just about Foreign Service officers, but this would also apply to civil servants as well, according to my understanding of it – but where we would try to build up relationships with various institutions, where we would go out and basically do recruiting, talk to different students on different campuses and so forth. One of the things that they want to do is hold minority-focused job fairs and see that as a way of helping to introduce the State Department to people who may not normally know about the State Department and know about careers available here.

    Another interesting idea the Secretary brought up was looking to our veterans, our veterans across the country, many of whom are getting out of the military and are looking for a civilian career now. They are a talented, important work pool, a workforce that knows how to get things done and knows how to get things done in difficult circumstances, and that really mirrors what we do here at the State Department. So the Secretary has talked about how he wants to try to recruit veterans and bring in veterans. So those are just kind of among the big toplines that we would focus on here.

    QUESTION: Right, but for the students that’s clearly a multiyear process, because you’re not going to be able to get these people in and then get them into senior positions where, if the stat is correct that he mentioned, only 12 percent of the senior Foreign Service is non-white, which is far more pale, male, and Yale than I actually ever thought it was, but – and I’ve been here for quite a long time.

    MS NAUERT: I know.

    QUESTION: But the issue that – or the question I have is: Previous Secretaries have tried to do exactly the same thing, and this veterans idea is not new, and in fact, veterans get preference for hiring in all federal civilian jobs. But there was a particular push in this building years ago, and it still doesn’t seem to have worked. So I guess my question is what exactly is going to be different this time around, because we had Secretary Powell notice this and see it, Secretary Rice too, and so —

    MS NAUERT: Matt, I’d have to go back and look – I’d have to go back and look at the numbers, the recruiting numbers and then the number of people who actually joined the State Department, the Foreign Service, and other programs that we have here, to see where it is now compared to where it was five, 10 years from now. So I’d have to go back and actually look at the data and compare the program that the Secretary has outlined – again, broad brush strokes, but outlined now – compared with the programs before. If you want me to do that, I can take a day or so to dive into that and try to figure it out, but I know that this is something that the Secretary —

    QUESTION: If I say yes, you’ll never talk to me again, right?

    MS NAUERT: No, of course I will. But it would take me some time to figure all that stuff out. That would be data-driven. But I know – I can tell you that this is important to the Secretary and this is something that he really wants to do.

    QUESTION: Right. But I – well, I – I mean, you don’t have to personally do it. Perhaps there is some way to quickly find out whether the numbers of the – minority numbers have been going up or going down or have been static over the course of the years despite these programs.

    MS NAUERT: Look, I’m not going to promise you that today, but we can certainly look into it. Okay? Okay. And you reporters out there, don’t start writing this and give me a deadline of 5 o’clock today, because it’s going to take a while to hunt down those numbers.

    QUESTION: (Off-mike.)

    MS NAUERT: Okay. Okay. Thank you, Matt.

    Okay. Sir, hi. How are you?

    QUESTION: China.

    MS NAUERT: China. Okay.

    QUESTION: Can I ask one more question on Charlottesville?

    MS NAUERT: Sure, of course.

    QUESTION: Does the Secretary ever plan to publicly address what he thought of President Trump’s remarks about Charlottesville? And do you know if – since they speak so frequently, do you know if he has had a private conversation with him telling him what he thought of – specifically about his remarks? Not just the incident itself, but the reaction.

    MS NAUERT: Yeah. A couple things. I know the Secretary has spoken with the President this week – not in person, but he’s spoken with him by phone. I’m not aware of whether or not it was a one-on-one call or whether it was just a group call, like a principals’ call or something of that sort, but I know he has spoken with the President this week. As you know, right now he’s at Camp David, and that’s where we’re – they’re having conversations, so that conversation may be going on at this time. I know that the Secretary has spoken out on two occasions about race this week alone: one as he was meeting with the foreign minister from Canada, in which he addressed what happened in Charlottesville; and then I think his overall views on race and diversity and the place in America that it properly holds today. So I think the Secretary has spoken a fair bit about that.

    Okay. Hi.

    QUESTION: Do you have any more on how the ambassadors are going to be – the pool is going to be selected, how that – having a minority in that group with the —

    MS NAUERT: Oh, I’m glad you asked that. One of the things the Secretary mentioned today is that when we look at our ambassadorial candidates at that pool, that the Secretary wants to have someone who represents a minority represented in those interviews to be interviewed for the job. And the Secretary said perhaps if that person is not ready yet for that position, that gives us a good opportunity to know who that person is and have that person on our radar and help bring that person along into the future. So it helps to identify a quality base of candidates and helps the State Department to better work with them to get them to that position which they aspire to.

    QUESTION: Is that effective immediately?

    MS NAUERT: I don’t know. I don’t know. I didn't get a chance to ask him that. Okay.

    Hi, Laurie.

    QUESTION: There are reports that Turkey is attacking the Syrian Kurdish city of Ephraim. Is that what’s going on? And if so, what is your reaction? What is happening in Ephraim?

    MS NAUERT: So I’ve seen that report, and I – I’m afraid I just don’t have anything for you on that right now.

    QUESTION: Well, then, I have another question.

    MS NAUERT: Okay, go right ahead.

    QUESTION: Thanks. Iraq has requested – has formally requested the UN’s help in investigating ISIS for war crimes. Can you give us some idea of what the next steps are going to be and what your role is going to be in that?

    MS NAUERT: So one of the things we’ve addressed here before is the amount of aid that we’ve helped to provide to Iraq, I believe also through the United Nations as well. I would need to double check on that. I have it in my notes somewhere. And part – what that is – the aim of that is to help the Iraqi Government and to help the United Nations to be able to identify some of those who have been involved in these – what we can call war crimes, genocide, and all of that.

    So the United States is putting financial aid so that they can – they can kind of better handle that situation.

    QUESTION: So how does this request to the UN change things, does it get more parties involved, make it formal?

    MS NAUERT: Yeah – I’m not sure exactly. So I’d have to just look into that further and get back to you on it. Okay?

    QUESTION: (Off-mike.)

    MS NAUERT: Okay. Hi. Hi. What’s your name?

    QUESTION: Omur Sahin from BirGun, a Turkish newspaper.

    MS NAUERT: Okay.

    QUESTION: I’m going to ask about the Reuters interview with Syria Democratic Forces spokesperson, that he said —

    MS NAUERT: An interview with who?

    QUESTION: With Syria Democratic Forces spokesperson.

    MS NAUERT: Okay.

    QUESTION: He said the U.S. will remain long after ISIS is defeated. I’m going to ask if you have a comment on that. And also, are you having some discussions with Syria Democratic Forces about your further plans in the region?

    MS NAUERT: Are we having conversations with who?

    QUESTION: With Syria Democratic Forces.

    MS NAUERT: Oh, with the Syrian Democratic Forces —

    QUESTION: Yeah.

    MS NAUERT: — about – okay. So the United States and coalition partners work with the SDF, the Syrian Democratic Forces, and the main goal in working with that entity, that group, was to take back Raqqa. We know that they are tried and true and tested, battle tested, battle ready, to take out ISIS, and they’ve done a good job of that. That operation, of course, is still underway to take out ISIS from Raqqa. So we have worked with them. We see that as something that’s being done in a very focused fashion and not in broader fashion.

    In terms of what you are referring to – that interview – I’m familiar with that interview, and let me just kind of point back to what one of our colleagues, someone over at Department of Defense, was talking about and that is our overall mission. And our overall mission, and we’re not taking our eye off the ball in this regard, is to defeat ISIS. Whether it’s in Iraq or in Syria, that is our intent, to defeat ISIS and not do anything more than that. We want Syria governed by Syrians, not by the United States, not by any other forces, but by Syrians.

    QUESTION: So you say you’re not planning to stay after defeating ISIS?

    MS NAUERT: Look, that is not our plan. Our intent is to defeat ISIS, and we’re keeping our focus on that.

    Okay, hi.

    QUESTION: China?

    MS NAUERT: Yes.

    QUESTION: Does the Secretary —

    MS NAUERT: Oh, wait. By the way, anything else on Syria?

    Okay, go right ahead.

    QUESTION: Does the Secretary believe that U.S. is at economic war with China?

    MS NAUERT: I’m sorry?

    QUESTION: Does the Secretary believe that the U.S. is at economic war with China?

    MS NAUERT: I have not asked him that question. I think what you’re probably referring to is our – Mr. Lighthizer, who handles trade for us. Is that – is that what you’re trying to get at?

    QUESTION: But does China pose any kind of economic national security —

    MS NAUERT: I have not – I have not asked the Secretary that. I know the Secretary continues to recognize China as a country we can have close cooperation with on many issues, on many fronts. They’ve been extremely helpful to us now in dealing with DPRK and – but again, I haven’t asked him that question.

    I know that the administration overall looks at China and looks at some of its trade practices and has concerns about it, and that’s a matter that other institutions are going to take up within the U.S. Government.

    QUESTION: I don’t think he was referring to Mr. – the trade representative, Mr. Lighthizer. I think he was referring to a view of China expressed by the until-several-hours ago chief strategist of the White House in an interview —

    MS NAUERT: Okay.

    QUESTION: — in an interview with a magazine in which the – this now former official also went after a career State Department official who handles China.

    MS NAUERT: Now I understand what you’re —

    QUESTION: Do you have —

    MS NAUERT: Now I understand what you’re talking about. My apologies, sir.

    QUESTION: Do you – or do you know, does the Secretary have a view on those comments? He said yesterday that he had seen them. I’m wondering if he does have a – if he – does he share the view of China that the former chief strategist of the President evinced?

    MS NAUERT: That he what?

    QUESTION: Evinced. That he spoke about to the magazine, that the —

    MS NAUERT: Which one – which part – portion of those comments in particular are you referring to?

    QUESTION: The – that the United States is at – in an economic war with China.

    MS NAUERT: I have not asked the Secretary that question. He’s not here right now. He mentioned that he’s aware of the comments, but we’ve been focused on a lot of bigger things – bigger things meaning DPRK, and bigger things in terms of what’s going on today and their meeting with the President today.

    QUESTION: Right.

    QUESTION: On that issue – sorry —

    MS NAUERT: Yeah. Hi.

    QUESTION: — it was pretty well-known when he said it in the interview that Mr. Bannon opposed the role that Susan Thornton was playing. Now that he’s removed – and it’s quite well-known that Secretary Tillerson favored Ms. Thornton to be the actual assistant secretary for East Asian affairs as opposed to acting – does the Secretary now see the way clear for her to take that position officially?

    MS NAUERT: Susan Thornton is fantastic. A lot of us have worked here quite closely with Susan. Susan’s been a part of the tip of the spear in dealing with the DPRK and she’s done it – she makes it look like it’s effortless and I cannot imagine that it is. But she handles herself very, very well, and she happens to be a very smart and accomplished woman as well. The news about Mr. Bannon broke about 11 o’clock today. The Secretary landed – or arrived at Camp David sometime after that or not long thereafter, so we have not had a chance to talk about this in particular.

    QUESTION: Thank you.

    MS NAUERT: Okay. Hey, how are you?

    QUESTION: Thanks. I want to go over the meetings that happened yesterday —

    MS NAUERT: Okay.

    QUESTION: — the 2+2. First, I just wanted to know if you had, like, a readout about how the meetings went. Did they go as expected? And then also, within the joint statement, I noticed that THAAD was never mentioned. And I didn’t know, was that never brought up during these meetings, or what’s the situation with that?

    MS NAUERT: Okay. A couple things: For the meeting that took place yesterday between the two-by-two – excuse me, the 2+2 between the Secretary and his counterpart, and also Secretary Mattis and his counterpart as well, they defined their shared roles, their missions, their capabilities, under the alliance that was going forward.

    As you know, the Secretary then met later on in the afternoon with his counterpart and their staffs as well. They talked about the strong trade and investment relationship between the United States and Japan, they talked about the administration engaging Japan to reduce barriers to trade and investment, they talked about enhancing economic and job growth in the United States and the region. They also touched upon DPRK. I was sitting in the meeting and I don’t recall the topic of THAAD coming up, but if one of them isn’t going to raise it, then they’re not going to raise it.

    QUESTION: Okay.

    QUESTION: Kono?

    MS NAUERT: Okay. I hope that answers your question. Hi.

    QUESTION: Yesterday during the press conference, both sides actually raised their concern in East and South China Sea. So today the Chinese foreign ministry’s spokesperson said United States and Japan, which are not parties in South China Sea, should respect the effort made by countries in the region to solve the issues peacefully with their – through coordination and negotiation. I wonder if you have a response to that.

    MS NAUERT: So in terms of the South China Sea, our position remains the same. Nothing has changed with regard to that, and we’ve talked about it many times here and so I’d just prefer to leave it at that.

    QUESTION: And particularly in the joint statement, there was this line mentioned that both sides recalled the incidents in 2016 August. I wonder, because it has been a year – I’m referring to the Senkaku/Diaoyu Islands – and I wonder what’s – since it’s been a year, what’s the urgency and need for United States and Japan to bring this – brought up this issue again? And also, they both highlighted the article of the mutual defense treaty between Japan and United States and they also especially emphasized Article 5. So what’s the reason behind it? I wonder if you could elaborate.

    MS NAUERT: So in terms of the Senkaku Islands, our position on that is – has not changed, and that has been clear, I think, all along. They’ve been under Japanese administration since the reversion of Okinawa back in 1972. They fall within the scope of Article 5, so that’s the – the technical definition or what encompasses the governing of that – of the 1960 U.S.-Japan Treaty of Mutual Cooperation and Security. So we oppose any unilateral action that seeks to undermine Japan’s administration of those islands.

    QUESTION: Thank you.

    MS NAUERT: Okay. Thank you. Hi, sir.

    QUESTION: Can we move to North Korea?

    MS NAUERT: Sure.

    QUESTION: So a couple of days ago, Foreign Minister Lavrov in a statement to TASS made a very interesting statement basically saying, we cannot support the ideas that some of our partners continue to put forward and that literally aim to economically strangle North Korea. Now, it seems that the United States position is to economically strangle North Korea until they come to the denuclearization and stop their missile programs. So how do you square that? What do your – what’s your response to this?

    MS NAUERT: And remind me, you work for who again?

    QUESTION: Yomiuri Shimbun, Japanese newspaper.

    MS NAUERT: Japanese. Okay. So I just ask that because you’re reading the Russian talking points – (laughter) – so that’s why I wanted to know about that.

    Look, it’s not just the United States. The DPRK would like to paint this as a conflict or as a stressor between the United States and the DPRK. It is hardly that. The entire world looks at what North Korea has been doing in terms of its illegal nuclear and ballistic missiles programs, and see – the entire world sees that as a threat. We saw that at the UN Security Council through its resolution.

    One of the ways that we believe that we can help get Kim Jong-un to the table to start negotiate is by showing him the repercussions of his actions, and the repercussions of his actions – he can – we will increasingly make the situation difficult for him. By that, I mean they get their money, they bring their money in, and it funds their weapons programs. By tightening the belt on North Korea, by ensuring that they don’t take in as much money as they have in the past, that helps to reduce the amount of money going into their weapons program. That we see as a key threat. The Secretary has talked about that; Secretary Tillerson, Secretary Mattis in their op-ed earlier this week. That’s one way that we can address the issue. And Kim Jong-un can see how isolated he will become – not just from the United States, but the world – if he maintains that.

    QUESTION: Was that really just earlier this week?

    MS NAUERT: I know.

    QUESTION: It seems like – (laughter) —

    MS NAUERT: I know. That was Monday.

    QUESTION: It seems like a long time ago.

    MS NAUERT: It does feel like a long week, doesn’t it?

    QUESTION: Time has no meaning anymore.

    MS NAUERT: Okay. Well, I’m sensing everybody’s a little sleepy here on a Friday. It’s a summer Friday in August, so thanks, everybody, for coming in. We sure appreciate it.

    QUESTION: Isn’t no briefing on Fridays an August old tradition?

    MS NAUERT: I would – (laughter) – but you know what, Elise? So many of you wanted to do more. Okay?

    QUESTION: It’s true. It’s why we’re all here.

    MS NAUERT: So look, look, let’s just – but wait, let’s just back up for a second.

    QUESTION: That’s why we’re all here.

    MS NAUERT: Let’s just back up for a second and take a look at this week. Okay? So Tuesday, we had our briefing, right. Wednesday, I went over to the Foreign Press Center and spent some time with just a couple of you but some other folks, so that was fantastic to be over there. Yesterday, we had the 2+2 with Secretary Tillerson and his counterparts. And then today, we had the briefing and, by the way, brought in Mark Green, the new USAID administrator, to speak with many of you.

    QUESTION: Can I ask —

    MS NAUERT: So – hold on – thank you all for all the engagements that you’ve been involved with, and we’ve been trying to bring as much as we can.

    QUESTION: Thank you.

    MS NAUERT: Okay. Go right ahead.

    QUESTION: Thank you.

    QUESTION: A Russia question real quick? So the – (laughter) —

    MS NAUERT: We said goodbye already.

    QUESTION: This is a little sudden. So the drawdown is in process; it has to be done by September 1st. Do you all have any sense yet of which of the – there’s three consulates and an embassy – which posts you’re removing people from, what the mix is?

    MS NAUERT: I – look, I don’t have anything for you on that. I know that we have agreed to provide a response to the Russian Government by September the 1st, and we so we plan to adhere to that, and that’s all I have.

    Okay. Thanks, guys.

    QUESTION: Thank you.

    MS NAUERT: Have a great weekend.

    QUESTION: You too.

    (The briefing was concluded at 2:57 p.m.)

    DPB # 45

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NAAQS Attainment and the PM2.5-Mortality Association

Background. Ambient air quality has been steadily improving since promulgation of National Ambient Air Quality Standards (NAAQS) by EPA in accordance with the Clean Air Act. In 1997, a standard for fine particulate matter (PM2.5) was promulgated for the first time. Although the impacts of this pollutant on health are well characterized, less is known whether the air pollution standards have resulted in improvements to public health. The objective of this study is to examine whether the attainment of the 1997 PM2.5 NAAQS improved cardiovascular mortality.
Methods. We examined the impact of change in PM2.5 on change in cardiovascular mortality rate before and after 2005, when the 1997 standard designations were published (2000-2004 vs 2005-2010). We further examined how the association varied with respect to county-level NAAQS designations by stratifying in two ways: first, by the EPA Green Book status of attainment or nonattainment; second, by the county-level design values (DV) used for designation. We used linear regression and difference-in-difference models, adjusted for sociodemographic confounders.
Results. Across the 619 U.S. counties with available PM2.5 data we observed a 1.21 µg/m3 mean decrease in the annual PM2.5 after 2005. Cardiovascular mortality rate, expressed as number of deaths/100,000 people, decreased by 63.1(95% CI 62.2, 64.1) in absolute terms after 2005 and by 1.10 (0.37, 1.82) for each 1 µg/m3 decrease in PM2.5. Nonattainment counties had twofold larger reduction in mean annual PM2.5 in nonattainment counties, 2.69 µg/m3, compared to attainment counties, 1.35 µg/m3. Nonattainment counties also had a greater absolute decrease in mortality rate, 63.7(62.2, 65.3), compared to attainment counties, 62.7(61.5, 64.0). However, per 1 µg/m3 decrease in PM2.5, nonattainment counties had a smaller change in mortality rate, 0.59(-0.54, 1.71), than attainment counties, 1.96(0.77, 3.15), though none of the differences were statistically significant. Similar results were observed when counties were stratified on the design values. Counties with DV greater than 15 µg/m3 experienced the greatest decrease in mean annual PM2.5 (3.09 µg/m3) and the largest improvement in the adjusted mean cardiovascular mortality, 64.5(62.5, 66.6), but the smallest decrease per 1 µg/m3 decrease in PM2.5 0.73(-0.57, 2.02).
Conclusions. Our findings suggest that counties designated nonattainment had a greater drop in mean PM2.5, greater absolute drop in mean cardiovascular mortality rate, but smaller incremental change in mortality rate per 1 µg/m3 PM2.5 compared to counties in attainment. Additionally, the change in PM2.5 values after the implementation of the NAAQS was strongly correlated with the DVs used for designation. Taken together, the results suggest that there is a non-linear relationship between the change in PM2.5 and the change in cardiovascular mortality. This study contributes to the di

Association of land use and beach closure in the United States

Swimming in natural waters (e.g., oceans, lakes, rivers) is one of most popular recreational activities in the United States. However, exposure to pathogens (e.g., Salmonella spp., Shigella spp., Cryptosporidium, Giardia, adenovirus, norovirus) in recreational waters can lead to a variety of adverse health outcomes. To protect public health and reduce the number of outbreaks associated with recreational waters, the BEACH Act was passed in 2000, which required beach regulators to develop a formal plan to assess beach water quality and to notify the public if recreational waters are unsafe. High levels of microorganisms in water often follow extreme weather events. Besides extreme weather events, the proximity of certain land uses to beaches may also have great influence on beach water quality. Microbial contaminants that lead to beach closures and human illness come mainly from land, either from discrete point sources or from diffuse non-point sources. It is expected that land use will have considerable influence on beach microbial water quality. However, to date, studies on impacts of land use on beach microbial contamination are rare, and few researchers are aware of the relationship between land use and beach closures.In this study, we analyzed beach closure data obtained from 2004 to 2013 for more than 500 beaches in the United States, and examined their associations with land use around beaches in 2006 and 2011. The results show that the number of beach closures due to elevated indicators of health risk is negatively associated with the percentages of forest, barren land, grassland and wetland, while positively associated with the percentages of urban area. The examination of the change of land use and the number of beach closures between 2006 and 2011 indicates that the increase in the number of beach closures is positively associated with the increase in urban (β=1.612, p<0.05) and agricultural area including pasture (β=0.098, p<0.05), but negatively associated with the increase in forest area (β= -1.789, p<0.05). The study suggests that urbanization and agriculture development near beaches have adverse effects on beach microbial water quality, while afforestation may protect beach water quality and reduce the number of beach closures.This abstract has been reviewed and approved by the U.S. Environmental Protection Agency. Its contents do not necessarily reflect the views and policies of the Agency.

Cardiovascular hospitalizations and associations with environmental quality

Cardiovascular disease has been identified as a condition that may be associated with environmental factors. Air pollution in particular has been demonstrated to be associated with cardiovascular disease and atherosclerosis, which can increase the likelihood of cardiovascular events. We examined the relationship between environmental quality and cardiovascular hospitalizations among Medicare recipients for 2010. Daily counts of hospitalizations for cardiovascular disease from the Center for Medicare and Medicare Services were used to calculate the proportion of Medicare recipients from U.S. counties (n=3,140) who were hospitalized due to any cardiovascular outcome in 2010. Cumulative environmental quality for 2000-2005 was characterized by five domain indices of the Environmental Quality Index (EQI): air, water, land, built, and sociodemographic domains. We used linear regression to estimate county-level prevalence differences in cardiovascular hospitalizations and 95% confidence intervals for quintiles of the overall EQI, as well as domain-specific EQI indices, all models controlled for county population percent minority and were clustered by climate region. We observed a negative prevalence difference (-0.28% [-0.46, -0.11]) for the overall EQI when comparing the quintiles with the worst environmental quality to the best environmental quality. However, when examining domain-specific measures, we observed a positive prevalence difference within the air domain (1.16% [0.97, 1.35]), the land domain (0.15% [-0.05, 0.35]), and the built environment domain (0.36% [0.19, 0.53]), when comparing worst environmental quality to the best environmental quality. The other domains, water and sociodemographic, had strongly negative associations with cardiovascular hospitalizations. Hospitalizations for cardiovascular disease among Medicare recipients may be associated with some factors related to environmental quality.This abstract does not necessarily reflect EPA policy.

Personal Care Product Use in Men and Urinary Concentrations of Select Phthalate Metabolites and Parabens: Results from the Environment And Reproductive Health (EARTH) Study

Author Affiliations open

1Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

2Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

3Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

4Channing Division of Network Medicine, Brigham and Women’s Hospital & Harvard Medical School, Boston, Massachusetts, USA

5Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts, USA

6Department of Growth and Reproduction & EDMaRC, Rigshospitalet University of Copenhagen, Copenhagen, Denmark

7National Center for Environmental Health, Centers for Disease Control and Prevention, Atlanta, Georgia, USA

8Department of Epidemiology, School of Public Health, Brown University, Providence, Rhode Island, USA

9Vincent Obstetrics and Gynecology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA

PDF icon PDF Version (2.2 MB)

  • Background:
    Personal care products (PCPs) are exposure sources to phthalates and parabens; however, their contribution to men’s exposure is understudied.
    We examined the association between PCP use and urinary concentrations of phthalate metabolites and parabens in men.
    In a prospective cohort, at multiple study visits, men self-reported their use of 14 PCPs and provided a urine sample (2004–2015, Boston, MA). We measured urinary concentrations of 9 phthalate metabolites and methylparaben, propylparaben, and butylparaben. We estimated the covariate-adjusted percent change in urinary concentrations associated with PCP use using linear mixed and Tobit mixed regressions. We also estimated weights for each PCP in a weighted binary score regression and modeled the resulting composite weighted PCP use.
    Four hundred men contributed 1,037 urine samples (mean of 3/man). The largest percent increase in monoethyl phthalate (MEP) was associated with use of cologne/perfume (83%, p-value<0.01) and deodorant (74%, p-value<0.01). In contrast, the largest percent increase for parabens was associated with the use of suntan/sunblock lotion (66–156%) and hand/body lotion (79–147%). Increases in MEP and parabens were generally greater with PCP use within 6 h of urine collection. A subset of 10 PCPs that were used within 6 h of urine collection contributed to at least 70% of the weighted score and predicted a 254–1,333% increase in MEP and parabens concentrations. Associations between PCP use and concentrations of the other phthalate metabolites were not statistically significant.
    We identified 10 PCPs of relevance and demonstrated that their use within 6 h of urine collection strongly predicted MEP and paraben urinary concentrations.
  • Received: 16 November 2016
    Revised: 05 April 2017
    Accepted: 06 April 2017
    Published: 18 August 2017

    Address correspondence to F.L. Nassan, Dept. of Environmental Health, Harvard T.H. Chan School of Public Health, 665 Huntington Ave., Building 1, Room 1406, Boston, MA 02115 USA. Telephone: (857) 244-3312. Email:

    Supplemental Material is available online (

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

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There is ubiquitous general population exposure to ortho-phthalates (hereafter referred to as phthalates) and several parabens (CDC 2017). Phthalates are a family of chemicals commonly used as plasticizers in polyvinyl chloride plastics and in consumer products, including personal care products (PCPs), medications, and food processing and packaging materials (CDC 2017). Diethyl phthalate (DEP) is the most commonly used phthalate in PCPs (FDA 2014b). Parabens are a family of chemicals with antimicrobial preservative properties that are also widely used in PCPs, pharmaceuticals, food, and beverages to increase the shelf life of the product (Dodson et al. 2012; FDA 2014a; Guo and Kannan 2013; Moos et al. 2014). Methylparaben, propylparaben, and butylparaben are most commonly used in PCPs (Braun et al. 2014; Dodson et al. 2012; Ferguson et al. 2017).

In multiple urine samples collected from two men and three women, Koch et al. (2013) found that concentrations of the metabolites of low molecular weight phthalates such as DEP, di-n-butyl phthalate (DnBP), and di-isobutyl phthalate (DiBP) had a cyclical pattern of rise and decline suggestive of ongoing repeated nonfood exposures. Koch et al. (2013) also found that monoethyl phthalate (MEP; a metabolite of DEP), concentrations increased following showers, which suggested PCPs as a major source of DEP exposure. Koch et al. (2014) also found that urinary concentrations of the parabens were lower when the participants were given products without parabens.

Exposure to phthalates and parabens from PCP use occurs through direct dermal application (Janjua et al. 2008; Seo et al. 2016), inhalation, oral ingestion, or even transdermal exposure from air (Weschler et al. 2015). Phthalates and parabens have short biological half-lives (6–24 h) (Janjua et al. 2008; Koch et al. 2012; Moos et al. 2016), and urinary concentrations of phthalate metabolites and parabens are the preferred exposure biomarkers (Calafat et al. 2015; CDC 2017).

Certain phthalates and parabens are endocrine disruptors and have been linked to adverse health outcomes (Boberg et al. 2010; Hannas et al. 2012; Howdeshell et al. 2016; Lioy et al. 2015; Orton et al. 2014; Zoeller et al. 2014). Identifying the most important sources of phthalate and paraben exposure is of paramount importance given the widespread use and potential health effects of these chemicals. Prior epidemiological studies on the contribution of PCP use to exposure to phthalates and parabens focused primarily on women and children (Braun et al. 2014; Buckley et al. 2012; Ferguson et al. 2017; Harley et al. 2016; Just et al. 2010; Martina et al. 2012; Philippat et al. 2015; Romero-Franco et al. 2011; Sathyanarayana et al. 2008). However, there have been limited studies in men (Duty et al. 2005; Ferguson et al. 2017). Given both differences in type and frequency of PCP use across both sexes, better understanding of the specific PCPs contributing to urinary phthalate metabolite and paraben concentrations in men is necessary.

Given this gap in knowledge on the contribution of PCP use to exposure to phthalates and parabens in men, we explored the association between self-reported use of 14 PCPs and urinary concentrations of 9 urinary phthalate metabolites and 3 parabens in men recruited as part of a prospective cohort study. Because of the known use of DEP, DiBP, and parabens in PCPs (Dodson et al. 2012; Wittassek et al. 2011), we focused on MEP, mono-isobutyl phthalate (MiBP; a metabolite of DiBP), methylparaben, propylparaben, and butylparaben. We considered including mono-n-butyl phthalate (MnBP; a metabolite of DnBP), but because DnBP is infrequently used in PCPs (FDA 2014b), it was not a focus of our analysis.



The Environment And Reproductive Health (EARTH) study (2004–present), a prospective cohort, enrolled couples seeking fertility treatment at the Massachusetts General Hospital (MGH) Fertility Center to identify determinants of fertility (Braun et al. 2014; Dodge et al. 2015). Among male partners who were 18–55 y of age, approximately 50% agreed to participate. Male participants were followed from study enrollment until their partner had a live birth or the couple discontinued treatment at MGH. Approximately 30% of EARTH men had a primary diagnosis of male factor infertility (Dodge et al. 2015). In the current analysis, men were eligible if they provided at least one urine sample and completed the PCP questionnaire [see “Product Use Questionnaire in the Environment And Reproductive Health (EARTH) Study” in the Supplemental Material] between December 2004 and June 2015. All men provided informed consent. The EARTH study was approved by institutional review boards at MGH, the Harvard T.H. Chan School of Public Health, and the Centers for Disease Control and Prevention (CDC).

Personal Care Product (PCP) Use Questionnaire

Upon enrollment, men completed questionnaires that collected information on demographics, lifestyle, and health information, and a research nurse measured their height and weight. At recruitment and at each subsequent visit, men completed a questionnaire on PCP use within the past 24-h and at what time they last used each PCP prior to the collection of each urine sample. There were questions for the use of 16 PCPs, but in the analysis we excluded toothpaste (used in 98% of men) and nail polish (<1% use). Therefore, we included deodorants, shampoo, conditioner/crème rinse, hairspray/hair gel, combined other hair care products (including mousse, hair bleach, relaxer, perm, and straightener), shaving cream, aftershave, cologne/perfume, mouthwash, bar soap, liquid soap/body wash, hand sanitizer, hand/body lotion, and suntan/sunblock lotion.

Based on our previous publication that showed higher urinary concentrations of MEP following PCP use in men from the same fertility clinic population (Duty et al. 2005) and another study that showed higher urine concentrations following dermal exposure through lotion application (Janjua et al. 2007), we a priori decided to explore both a 6-h and a 24-h time window for PCP use before the urine collection. Furthermore, in women from the same fertility clinic population, we reported higher urinary biomarker concentrations when the product was used within 6 h prior to urine sample collection (Braun et al. 2014).

Urinary Measurements of Phthalate Metabolite and Paraben Concentrations

At each visit, men collected urine in a sterile polypropylene cup using standard procedures. Study staff recorded the time of collection and measured specific gravity (SG) using a handheld refractometer (National Instrument Co. Inc.). Urine samples were divided into aliquots, frozen, and stored at −80°C before shipment on dry ice to the CDC. Briefly, the analytical technique for quantification of the urinary biomarkers involved enzymatic deconjugation of the urinary metabolites, followed by solid-phase extraction, separation by high performance liquid chromatography, and detection by isotope-dilution tandem mass spectrometry.

As previously described (Silva et al. 2007; Zhou et al. 2014), the CDC used solid-phase extraction–high performance liquid chromatography–isotope-dilution tandem mass spectrometry to quantify total (free plus conjugated) urinary concentrations (μg/L) of three parabens (methylparaben, propylparaben, and butylparaben) and nine phthalate metabolites: MEP, MnBP, MiBP, monobenzyl phthalate [MBzP; a metabolite of butylbenzyl phthalate (BBzP)], mono(2-ethylhexyl) phthalate (MEHP), mono(2-ethyl-5-oxohexyl) phthalate (MEOHP), mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP), mono(2-ethyl-5-carboxypentyl) phthalate (MECPP) [MEHP, MEOHP, MEHHP, and MECPP are all metabolites of di(2-ethylhexyl) phthalate (DEHP)], and mono(3-carboxypropyl) phthalate [MCPP; a nonspecific metabolite of DnBP, di-n-octyl phthalate (DOP), and other high-molecular–weight phthalates]. We also calculated the molar sum of DEHP metabolites (∑DEHP) (Braun et al. 2014). The limits of detection (LOD) ranged from 0.10 (propylparaben, butylparaben) to 1.20 μg/L (MEHP). Concentrations below the LOD were replaced by the LOD divided by the square root of 2 for all biomarkers detected in at least 90% of the samples (Hornung and Reed 1990; Lubin et al. 2004).

Statistical Analysis

We calculated descriptive statistics for participants’ baseline characteristics (such as age, race, education, BMI, and smoking); time-varying characteristics (such as time of the day, season, and calendar year of the sample collection); and personal care product use patterns. Because of the relatively short half-lives of phthalates and parabens (Janjua et al. 2008; Koch et al. 2012; Moos et al. 2016) and findings of previous publications, we defined PCP use as use within the last 24 and 6 h before urine collection. We used two approaches for assessing men’s use of PCPs. The first approach (single-PCP analyses) defined use of each PCP as a binary variable (yes/no use within the 24 h preceding urine collection). Because multiple PCPs contributed to the overall body burden of phthalates and parabens, in our second approach (multi-PCP analyses) we used a composite weighted PCP score (continuous variable) as explained below.

The nine urinary concentrations of phthalate metabolites, ∑DEHP, and three parabens were modeled as continuous variables. We examined distributions for the urinary concentrations, and because of observed skewness, we used a natural log-transformation to satisfy regression model assumptions. We examined the Spearman correlations among the concentrations of phthalate metabolites and parabens.

Covariate Adjustment

Selection of covariates was based a priori on directed acyclic graphs and previous literature (Braun et al. 2014; Duty et al. 2005; Smith et al. 2012). The final model included urine specific gravity (continuous) (O’Brien et al. 2016) to account for urine dilution, race (Caucasian or not), age (continuous), body mass index (BMI) (continuous), calendar year [continuous, because it has been shown that urinary concentrations of several phthalate metabolites have changed over time in the United States (Zota et al. 2014)], three categories for time of urine sample collection [early morning (between 0500 and 0900 hours), late morning (between 0901 and 1200 hours), or afternoon (after 1201 hours)], current smoking (yes/no), and warm season (April through September) (yes/no) to account for seasonality in Boston with higher use of certain products, specifically sunscreen, in the summer.

Multiple Imputations of the Data

Some men had incomplete PCP-use data across their repeated visits and two men were missing BMI. The missingness for each PCP question ranged from 5 (for shampoo) to 339 (for hand/body lotion). Distributions of the urinary concentrations of phthalate metabolites and parabens were not statistically different when comparing the complete and incomplete PCP-use data, suggesting that missingness was not related to our outcomes and would not likely impact our findings. We investigated missingness patterns for PCPs use and the concordance of PCP use within the same man over repeated visits. We used multiple imputation to account for the missing data, using multiple imputation by chained equations (MICE) (Sterne et al. 2009; White et al. 2011) for imputing missing PCP-use data and covariates (two men missing BMI). For any given PCP at any time point, the predictors were the same PCP used at other time points, urine specific gravity (O’Brien et al. 2016), race, age, BMI, calendar year, time of sample collection, current smoking, and season. We generated 10 imputed datasets for each analysis and used these imputed datasets in the main analysis using both of the analytical approaches described below using proc mianalyze in SAS (version 9.4; SAS Institute Inc., Cary, NC).

Regression Models Analysis

Single-PCP analyses.

We first regressed each biomarker (and ∑DEHP) concentration on a single-PCP and covariates in separate linear mixed effects models (LMEM) with a random intercept to account for within-person correlation among multiple longitudinal measures of a given biomarker. Because MEHP and butylparaben had >10% nondetectable concentrations, we used mixed Tobit regression (Tobin 1958) models with random intercepts for left-censored data (Lubin et al. 2004). For MEHP and butylparaben, we took into account LODs from different analytic batches over the study period by specifying a unique LOD for each sample collection within the Tobit regression. In addition, we regressed all biomarkers on a continuous variable for total sum (0 to 14) of the PCPs used by each man at each visit.

We created heatmaps for better visualization of the estimated percent change (% change) in the urinary concentrations associated with each PCP use based on LMEM (or Tobit models for MEHP and butylparaben). We adjusted for the covariates mentioned above in all models. We considered two-sided alpha <0.05 as statistically significant and indicated significance as asterisks in heatmaps.

We further explored the association between the PCP use within 6 h before urine collection (yes/no) for five of the urinary concentrations that were most likely to have PCP use as sources of their parent compound, that is, MEP (for DEP), MiBP (for DiBP), methylparaben, propylparaben, and butylparaben (Buckley et al. 2012; Dodson et al. 2012; Philippat et al. 2015; Wittassek et al. 2011).

Multi-PCP analyses.

To analyze the simultaneous impact of the 14 PCPs on a given biomarker concentration, we modified the weighted quantile score (WQS) regression model previously used for quantifying the impact of environmental mixtures on an outcome (Carrico 2013; Carrico et al. 2015; Christensen et al. 2013; Gennings et al. 2010, 2013). Given the high dimensionality and inherent correlations among the use of different PCPs as well as correlations among phthalate metabolites and parabens urinary concentrations, traditional regression is unsuitable (Carrico 2013). In addition, in previous simulation analyses, the WQS showed improvements over ordinary regression and LASSO (Carrico 2013). The WQS approach assumes that a given biomarker urinary concentration is associated with a PCP-use composite score, where this score encompasses a linear combination of PCP (0/1) use indicators. For a given urinary concentration, the approach estimates simultaneously PCP weights and an effect estimate for the resulting composite score within a regression model framework. Within each imputed data set, we used bootstrapping to empirically estimate the weights, and the data set was split (50/50) into a training set and a test set to provide valid inferences (e.g., p-values) for the estimated slope of the composite score. For biomarkers with a small percent of nondetectable concentrations (up to 10% below LOD), we constructed a weighted binary score (WBS) within the linear regression framework. For biomarkers with a moderate percent of concentrations below the LOD (>10% and <70% below LOD), we performed WBS estimation within the Tobit regression model.

We present results as weighted % change in urinary concentrations of MEP, MiBP and the three parabens associated with an increase in the weighted score of the 14 PCPs, as well as the corresponding weight for each PCP (sum to 100%). Similarly, we a priori chose to present the number of PCPs (could be different depending on the chemical) that summed to at least 70% of the weighted score. The 70% of the weighted score should provide a reasonable prediction of exposure as compared with 100% of the weighted score, and may therefore be applicable for future research on exposure from PCPs. We repeated these analyses for PCPs use with 6 h of urine collection for the five urinary concentrations of higher interest.

Sensitivity analyses.

Given the low detection frequency for butylparaben (31%), we performed a secondary analysis using generalized estimating equations (GEE) with logit link after we dichotomized butylparaben urinary concentrations into detectable versus nondetectable concentrations (Dodge et al. 2015). We adjusted for the same covariates as in the primary analyses. We report adjusted odds ratios of detection associated with PCP use along with p-values). We repeated all analyses using only the complete case analyses (with no imputation). Analyses were also repeated after imputing the missing PCP data as “0,” assuming missing was “no use.”

We conducted statistical analyses using SAS version 9.4 (SAS Institute Inc., Cary, NC) and used the (heatmap.2) R package for heatmap generation (version 3.1.2; R Development Core Team).


In the final analysis, 400 men contributed 1,037 urine samples with an average of 3 samples per man (up to 12 samples); 29% of the men provided 1 urine sample, 31% provided 2, and 40% provided at least 3. The median time between any two consecutive urine collections per man was 77 d (interquartile range: 34–126 d).

The men were mostly Caucasian (86%), nonsmokers (93%), and had a median age of 36 y, median BMI of 27 kg/m2, and a college or graduate education (84%) at the time of enrollment. Urine collection time ranged from 0600 to 1800 hours with 42% collected between 0900 and 1200 hours, and 46% of the samples collected in the warm season (Table 1).

Table 1. Demographics for 400 men who contributed 1,037 urine samples in the Environment And Reproductive Health (EARTH) study.
Baseline characteristics N (%) or mean±SD [range]
Age (years) 36.5±5.50 [23.9, 66.6]
 Caucasian 343 (86)
 Black/African American 13 (3)
 Asian 29 (7)
 Other 15 (4)
BMI (kg/m2)a 27.5±4.45 [18.6, 50.0]
BMI categories
 Underweight: <18.5 2 (1)
 Normal weight: 18.5≤BMI<25 114 (28)
 Overweight: 25≤BMI<30 192 (48)
 Obese: BMI≥30 92 (23)
Education categoriesa
 Less than college graduate 50 (16)
 College graduate 109 (34)
 Graduate degree 158 (50)
Current smoking status
 Yes 26 (7)
 No 374 (93)
Time-varying characteristics for urine sample collection n (%)
Warm season (April–September) 477 (46)
Calendar year
 2004–2009 522 (50)
 2010–2015 515 (50)
Time of the day
 Early morning: >0500 and ≤0900 hours 385 (37)
 Late morning until noon: >0900 and ≤1200 hours 432 (42)
 Afternoon: >1200 hours 220 (21)

Note: BMI, body mass index; N, men’s number; <emn. urine samples’ number; SD, standard deviation. N (number of men) or n (number of urine samples) (%) were used for categorical/binary variables and mean±SD [range] for continuous variables.

aTwo men missing information on BMI and 83 missing education.

Deodorants and shampoos were the most frequently (>80%) used PCPs. Suntan/sunblock lotion and other hair care products were the least frequently (<10%) used PCPs (Table 2). The concordance between specific PCP use at different study visits within the same men was relatively high, ranging from 47% for hand sanitizer to 93% for suntan/sunblock lotion.

Table 2. Self-reported use of 14 personal care products (PCPs) within 24 and 6 h of collection of urine sample among 400 men in the Environment And Reproductive Health (EARTH) study.
Personal care products (PCPs) n (answered yes/no) N (answered yes/no and reported time since last used) n (%) answered yes within 24 h n (%) answered yes within 6 h % concordanta over time
Deodorant 1,029 1,023 879 (85) 723 (71) 82
Shampoo 1,032 1,024 832 (81) 584 (57) 74
Conditioner/crème rinse 946 941 220 (23) 149 (16) 76
Hairspray/hair gel 952 948 291 (31) 222 (23) 83
Other hair care productsb 914 910 66 (7) 46 (5) 90
Shaving cream 993 993 390 (39) 263 (26) 62
Aftershave 800 799 79 (10) 53 (7) 87
Cologne/perfume 955 952 202 (21) 145 (15) 79
Mouthwash 975 969 325 (33) 199 (21) 72
Bar soap 1,003 1,001 755 (75) 567 (57) 81
Liquid soap/body wash 1,016 990 732 (72) 508 (51) 55
Hand sanitizer 726 714 219 (30) 128 (18) 47
Hand/body lotion 698 695 162 (23) 102 (15) 81
Suntan/sunblock lotion 863 862 25 (3) 12 (1) 93

Note: n, number of visits/urine samples; PCPs, personal care products.

aConcordant: same answer for product use within 24 h over different visits for the same man, only includes observations for men who completed at least two visits.

bCombined other hair care products included mousse, hair bleach, relaxer, perm, and straightener.

The percentage of samples (2004–2015) with detectable urinary concentrations of phthalates and parabens biomarkers ranged from 90% to 100% except for MEHP (78%) and butylparaben (31%) (Table 3). The men’s median urinary MEP and paraben concentrations were substantially lower than the medians for women from the same cohort (Braun et al. 2014) but comparable to male urinary concentrations from the National Health and Nutrition Examination Survey (NHANES) (CDC 2017) (Table 3). Spearman correlations were generally weak between phthalate metabolites and parabens (range: 0.09–0.32), weak to strong among different phthalate metabolites (range: 0.22–>0.99) with the strongest correlation occurring between metabolites from the same parent compound (e.g., DEHP), and moderate to strong among different parabens (range: 0.41–0.80) (see Figure S1). Strong correlations indicate similar sources and weak correlations indicate different sources of exposure. These correlations are in accordance with results shown in Figure 1.

Heatmap showing adjusted percentage change and use of personal care products.
Figure 1. Heatmap for adjusted % change in urinary phthalate metabolite and parabens concentrations associated with self-reported use of personal care products (PCPs) within 24 h of urine sample collection among 400 men who contributed 1,037 urine samples in the Environment And Reproductive Health (EARTH) study. Abbreviations: DEHP means, ∑DEHP metabolites (μmol/L)=sum of μmol/L of MEHP+MEOHP+MEHHP+MECPP; total products: the crude sum of PCP used within 24 h. Multiple imputation of the missing was based on concordance of product use within persons. For any given PCP at any time point, the imputation model included PCP use at other time points, urine specific gravity (continuous), race (Caucasian or not), age (continuous), BMI (continuous), calendar year (continuous), time of sample collection [early morning (>0500 and ≤0900 hours), late morning (>0900 hours and ≤1200 hours), or afternoon (>1200 hours)], current smoking (yes/no), and warm season (April–September) (yes/no). Analysis adjusted for urine specific gravity (continuous), race (Caucasian or not), age (continuous), BMI (continuous), calendar year (continuous), time of sample collection [early morning (>0500 and ≤0900 hours), late morning (>0900 hours and ≤1200 hours), or afternoon (>1200 hours)], current smoking (yes/no), warm season (April–September) (yes/no), and the product use within 24 h (yes/no). The last column for the total products represents % changes associated with each additional type of PCP used, regardless of which PCP. Urinary concentrations were ordered according to the molecular weights within phthalates and within parabens. Combined other hair care products included mousse, hair bleach, relaxer, perm, and straightener. Analysis was based on 10 imputed data using the chained equations method. *p-value<0.05. **p-value<0.01.
Table 3. Distributions of urinary concentrations of phthalate metabolites and parabens measured in 400 men (n=1,037 urine samples)a in the Environment And Reproductive Health (EARTH) study.
Urinary concentrationsb (μg/Lc) % (>LODd) GM 10th perc 25th perc 50th perc 75th perc 90th perc GM (NHANES)e 2005–2006 GM (NHANES)e 2009–2010 GM (NHANES)e 2011–2012
Monoethyl phthalate (MEP) 100 48.8 6.86 16.6 42.0 145 458 107 61.0 38.1
Mono-n-butyl phthalate (MnBP) 98 10.6 2.00 4.90 12.4 24.7 43.6 19.8 14.5 8.14
Mono-isobutyl phthalate (MiBP) 97 6.44 1.20 3.20 7.40 14.9 27.2 5.65 7.80 6.54
Monobenzyl phthalate (MBzP) 95 3.25 0.50 1.37 3.60 8.10 16.2 9.47 6.93 4.80
Mono(2-ethylhexyl) phthalate (MEHP) 78 3.03 <LOD 0.85 2.80 7.80 24.9 3.40 1.83 1.51
Mono(2-ethyl-5-oxohexyl) phthalate (MEOHP) 99 10.3 1.40 3.80 9.80 24.8 84.1 18.3 9.14 5.50
Mono(2-ethyl-5-hydroxyhexyl) phthalate (MEHHP) 100 17.3 2.40 6.00 16.2 45.3 148 29.6 15.2 8.71
Mono(2-ethyl-5-carboxypentyl) phthalate (MECPP) 100 27.7 4.30 10.5 26.1 65.5 209 43.6 23.4 14.3
∑DEHP (di(2-ethylhexyl) phthalate) metabolites (μmol/Lf) 0.20 0.03 0.07 0.19 0.49 1.57
Mono(3-carboxypropyl) phthalate (MCPP) 97 3.85 0.60 1.60 3.70 9.70 23.2 2.32 3.37 3.52
Methylparaben 99 28.0 4.70 9.40 23.2 80.4 226 29.8 31.7 23.2
Propylparaben 90 2.86 0.14 0.60 2.30 12.1 58.6 2.96 2.77 2.44
Butylparaben 31 0.26 <LOD <LOD <LOD 0.30 2.20 <LOD <LOD <LOD

Note: GM, geometric mean; LOD, limit of detection; NHANES, National Health and Nutrition Examination Survey; perc, percentile; SD, standard deviation.

aPhthalate metabolites were measured in 1,037 urine samples from 400 men and parabens in 965 samples from 383 men.

bUrinary concentrations were ordered according to the molecular weights within phthalates and within parabens.

cConcentrations in μg/L (except for ∑DEHP) were not corrected for urine dilution; concentrations <LOD were replaced by the LOD divided by the square root of 2.

dLOD range (in μg/L) for MnBP: 0.4, 0.6; MiBP: 0.2, 0.3; MBzP: 0.2, 0.3; MEHP: 0.5, 1.2; MEOHP: 0.2, 0.7; MEHHP: 0.2, 0.7; MCPP: 0.18, 0.2; M_pb: 1; B_pb: 0.1, 0.2; B_pb: 0.1, 0.2.

eConcentrations from NHANES (2005–2006), (2009–2010), and (2011–2012) because our data encompassed the years 2004–2015.

f∑DEHP metabolites (μmol/L)=μmol/L sum of MEHP+MEOHP+MEHHP+MECP.

The adjusted % changes in urinary concentrations (by the single-PCP approach) were highest for MEP (e.g., 83% increase with cologne/perfume use and 74% increase with deodorant use) (Figure 1). Hairspray/hair gel, aftershave, mouthwash, shaving cream, and other hair care products predicted MEP concentrations (18–31% increase). For the three parabens, the strongest predictors were use of suntan/sunblock lotion (66–156% increase) and of hand/body lotion (79–147% increase). Hairspray/hair gel, shaving cream, aftershave, mouthwash, and deodorant were moderate predictors for parabens. Liquid soap/body wash use was a strong predictor only for butylparaben (86%). Apart from hand sanitizer and bar soap, the rest of the PCPs were moderate to weak predictors of the three parabens. Each additional type of PCP used, regardless of which PCP, was associated with a 12% increase in MEP and a 14–29% increase in parabens urinary concentrations. Although shampoo use was significantly associated with a 33% increase in the urinary concentrations of MEHP, it was not significantly associated with the other DEHP metabolites. The associations between PCP use and urinary concentrations of other phthalate metabolites were weak to null, and sometimes even negative, but none were statistically significant. The percent increase in the urinary concentrations associated with PCP use within 6 h of urine collection was generally larger and more statistically significant than for use within 24 h (Figure 2).

Panels 2A, 2B, 2C, 2D, and 2E are bar graphs plotting percent change in urinary monoethyl phthalate concentration, urinary mono-isobutyl phthalate concentration, urinary butylparaben concentration, urinary methylparaben concentration, and urinary propylparaben concentration, respectively, (y-axis) associated with the use of personal care products, namely, deodorant, shampoo, conditioner/creme rinse, hairspray/hair gel, other hair care products, shaving cream, aftershave, cologne/perfume, mouthwash, bar soap, liquid soap/body wash, hand sanitizer, hand/body lotion, and suntan/sunblock lotion and with total products (x-axis).
Figure 2. Adjusted % change in urinary concentrations of parabens and phthalate metabolites associated with 14 personal care products used within 24 and 6 h of urine collection: (A) monoethyl phthalate (MEP) urinary concentration; (B) mono-isobutyl phthalate (MiBP) urinary concentration; (C) butylparaben urinary concentration; (D) methylparaben urinary concentration; and (E) propylparaben urinary concentration. Multiple imputation of the missing was based on concordance of product use within persons. For any given PCP at any time point, the imputation model included PCP use at other time points, urine specific gravity (continuous), race (Caucasian or not), age (continuous), BMI (continuous), calendar year (continuous), time of sample collection [early morning (>0500 and ≤0900 hours), late morning (>0900 hours and ≤1200 hours), or afternoon (>1200 hours)], current smoking (yes/no) and warm season (April–September) (yes/no). Analysis adjusted for urine specific gravity (continuous), race (Caucasian or not), age (continuous), BMI (continuous), calendar year (continuous), time of sample collection [early morning (>0500 and ≤0900 hours), late morning (>0900 hours and ≤1200 hours), or afternoon (>1200 hours)], current smoking (yes/no), warm season (April–September) (yes/no), and the product use within 24 h (yes/no). Total products: the crude sum of PCP used within 24 h. The total products’ bar graphs plotting % change associated with each additional type of PCP used, regardless of which PCP. Combined other hair care products included mousse, hair bleach, relaxer, perm, and straightener. Analysis was based on 10 imputed data using the chained equations method. *p-value<0.05. **p-value<0.01.

In the multi-PCP approach, we presented the different weights for each PCP depending on the association with each biomarker so that the 14 PCPs sum to 100%. When regressing these composite scores, we found that a 1-unit increase in the weighted score of the use of all 14 PCPs was associated with an increase in MEP and paraben urinary concentrations ranging from 700% to 1,398% for samples collected within 24 h of PCP use (Figure 3A; see also Table S1). When we limited PCP use to the last 6 h, a 1-unit increase in the weighted score was associated with a 458–3,626% increase in urinary concentrations of MEP and the parabens (Figure 3B; see also Table S1).

Four stacked bar graphs plotting percentage weight change (y-axis) of PCPs associated with the biomarkers MEP, MiBP, butyl paraben, methyl paraben, and propyl paraben. PCPs used within 24 hours (stacked bar graph 3A) and within 6 hours (stacked bar graph 3B) contributing 100 percent are deodorant, shampoo, conditioner/creme rinse, hairspray/hair gel, other hair care products, shaving cream, aftershave, cologne/perfume, mouthwash, bar soap, liquid soap/body wash, hand sanitizer, hand/body lotion, and suntan/sunblock lotion. PCPs used within 24 hours (stacked bar graph 3C) and 6 hours (stacked bar graph 3D) contributing atleast 70 percent are cologne/perfume, deodorant, aftershave, conditioner/creme rinse, other hair care products, hairspray/hair gel, hand/body lotion, shaving cream, suntan/sunblock lotion, hand sanitizer, and liquid soap/body wash.
Figure 3. Weights of personal care products (PCPs) that contribute 100% and at least 70% to the overall urinary concentrations of biomarkers associated with PCPs use within 24 h and 6 h of sample collection: A) weights of personal care products (PCPs) that contribute 100% to the overall urinary concentrations associated with PCPs use within 24 h and their overall weighted % change; B) weights of personal care products (PCPs) that contribute 100% to the overall urinary concentrations associated with PCPs use within 6 h and their overall weighted % change; C) weights of personal care products (PCPs) that contribute at least 70% to the overall urinary concentrations associated with PCPs use within 24 h and their overall weighted % change; D) weights of personal care products (PCPs) that contribute at least 70% to the overall urinary concentrations associated with PCPs use within 6 h and their overall weighted % change. Numbers presented inside the stack bars represent the weights associated with PCP use. The weights of the 14 PCPs are summed to ∼100 due to approximation. The percent presented above the stacked bars represent the % change associated with PCPs. The weights presented are NOT summed to the % change, instead summed to 100% for the 14 PCPs and 70% or more for the PCPs presented above. The % changes are calculated by exponentiation of the weighted beta coefficient based on the PCPs used given the weights. Multiple imputation of the missing was based on concordance of product use within persons. For any given PCP at any time point, the imputation model included PCP use at other time points, urine specific gravity (continuous), race (Caucasian or not), age (continuous), BMI (continuous), calendar year (continuous), time of sample collection [early morning (>0500 and ≤0900 hours), late morning (>0900 hours and ≤1200 hours), or afternoon (>1200 hours)], current smoking (yes/no), and warm season (April–September) (yes/no). Analysis adjusted for urine specific gravity (continuous), race (Caucasian or not), age (continuous), BMI (continuous), calendar year (continuous), time of sample collection [early morning (>0500 and ≤0900 hours), late morning (>0900 hours and ≤1200 hours), or afternoon (>1200 hours)], current smoking (yes/no), warm season (April–September) (yes/no), and the product use within 24 h (yes/no). Combined other hair care products included mousse, hair bleach, relaxer, perm, and straightener. Analysis was based on 10 imputed data using the chained equations method.

We also present in Figure 3C, 3D the adjusted weighted % change in the urinary concentrations associated with the PCPs making up at least 70% of the weighted score as contributed by the highest weighted PCPs for each chemical use within 24 and 6 h of urine collection, along with the weights for each PCP (sum to at least 70%). The adjusted weighted % change in the urinary concentrations of MEP and the three parabens was associated with six or fewer PCPs (depending on the biomarker) and ranged from a 111% to a 591% increase within 24 h of urine collection and up to a 1,333% increase within 6 h of urine collection. MiBP urinary concentration was not strongly predicted by the weighted PCP score.

For MEP and the three parabens individually, seven or fewer PCPs used within 24 or 6 h explained at least 70% of the weighted PCP score. The adjusted weighted % changes within 6 h were generally higher than within 24 h of urine collection. Overall, use of 10 PCPs within 6 h prior to collection explained at least 70% of the weighted score. These included cologne/perfume, deodorant, suntan/sunblock lotion, hand/body lotion, aftershave, other hair care products, mouthwash, conditioner/crème rinse, hairspray/hair gel, and liquid soap/body wash, which explained at least a 254% and up to a 1,333% increase in MEP and the three parabens urinary concentrations (Figure 3C, 3D).

Secondary analyses performed by modeling butylparaben as a dichotomous outcome (detected vs. nondetected) provided odds ratios that were consistent with the direction of the % changes from Tobit regression (see Table S2). Sensitivity analyses using the complete case analysis (with no imputation) and after imputing the missing PCP use as no use gave consistent results with the main analysis (see Figures S2–S5 and Tables S3–S6).


We found that self-reported PCP use among men was associated with higher urinary concentrations of MEP and three parabens (methyparaben, propylparaben, and butylparaben). As expected and consistent with a prior study of women in the same cohort (Braun et al. 2014), due to the short half-lives of phthalates and parabens (Janjua et al. 2008; Koch et al. 2012; Moos et al. 2016) and the episodic use of PCPs, observed associations were stronger for PCP use within 6 h of urine collection as compared with use 24 h prior to collection. PCP use did not predict MiBP or the other phthalate metabolite urinary concentrations, which is consistent with infrequent use of these phthalates in PCPs (Dodson et al. 2012).

To our knowledge only two large-scale studies have investigated the association between PCP use and urinary phthalate metabolites and parabens in men. Duty et al. (2005) explored associations of five PCPs used within 48 h of a single spot urine sample collection in 406 men recruited from the MGH Andrology Laboratory (2000–2003); cologne and aftershave were predictors of MEP urinary concentrations (Duty et al. 2005). More recently, using NHANES data, Ferguson et al. (2017) reported that mouthwash was a more important source of paraben exposure in men than in women. Our results were consistent with the findings from these previous studies even though both relied on only one spot urine sample per participant and had limited information about the time of PCP use relevant to the urine collection. Duty et al. (2005) assessed PCP use within 48 h before urine collection, which is likely to be too long a time window which would contribute to attenuation of associations. Ferguson et al. (2017) had no information on last time of use relative to urine sample collection. Instead, PCP use was recorded as always, sometimes, or never, introducing exposure misclassification that is likely nondifferential. Our study, with repeated urine samples from the same man and more detailed information on time of PCP use extends the literature by identifying specific predictors of phthalates and parabens exposure.

In our study, men’s self-reported information on PCP use was predictive of select phthalate (mainly DEP) and paraben exposures. Based on our results, the exposure assessment of phthalates and parabens can be improved by asking only about the most “important” PCPs used within 6 h before urine collection. We showed that 10 PCPs contributed to at least 70% of the weighted score and explained more than a 250% increase in the urinary concentrations of MEP and the three parabens. Our recommendation for refining the questionnaire includes focusing on the following 10 PCPs: cologne/perfume, deodorant, suntan/sunblock lotion, hand/body lotion, aftershave, other hair care products, mouthwash, conditioner/crème rinse, hairspray/hair gel, and liquid soap/body wash. Shortening the questionnaire would likely decrease missingness and improve participants’ response rate. In addition, restricting the period of inquiry to the 6 h prior to urine collection would provide a more precise estimate of exposure to phthalates and parabens because the urinary concentrations of the biomarkers are a better reflection of exposure in the last 6 h than in the last 24 h. It is also likely that participants will better recall their PCP use within a shorter time frame.

Our study had several potential limitations. First, our single-PCP approach may be susceptible to multiple testing statistical issues. However, we also used a multi-PCP approach using WBS regression to account for multiple testing and the resulting estimated % changes were even larger, making it unlikely that any false positives in the single-PCP analysis accounted for our findings. Butylparaben had a low detection frequency, which was consistent with findings from the NHANES (Calafat et al. 2010; CDC 2017; Ferguson et al. 2017) and a German study (Moos et al. 2015). We addressed this in two ways. First, to explicitly model nondetectable concentrations, we used Tobit regression, which accounts for missingness better than replacing those concentrations with the LOD divided by the square root of 2 and linear regression (Ferguson et al. 2017). Second, we applied a sensitivity analysis using GEE (detected vs. nondetected) as performed before (Dodge et al. 2015), and these sensitivity analyses produced results comparable to the Tobit regression.

We had no information about frequency of PCP use, amount of product used, whether it was used with hot or cold water, phthalates and parabens product content, or brand names of the PCPs. Nevertheless, the lack of this information would have likely introduced nondifferential measurement error and thus attenuated our results toward the null. In addition, knowing the brand name may not remedy this potential limitation given that even the same brand might change product formulations over time and chemicals are not always listed on product labels. Because of short half-lives and episodic use, urinary concentrations of the biomarkers will depend on the frequency of voids between PCP use and urine collection and we did not collect this information. We only focused on exposures from PCP use, not accounting for other exposure sources. However, this is less likely to affect our results for MEP and the parabens because the PCPs are considered a major source of exposure to these compounds (or their precursors) (Braun et al. 2014; Janjua et al. 2007, 2008; Just et al. 2010), whereas for other phthalates such as DEHP, food is a major source of exposure (Serrano et al. 2014). In addition, it is unlikely that other sources of exposure such as diet would meet the confounder definition because they are unlikely to be associated with PCP use, hence not affecting the study validity. Finally, most men in our study were Caucasian, overweight or obese, highly educated, and nonsmokers. Therefore, our results may be generalized only to men with similar characteristics.

Our study had several strengths, including the use of repeated collection of self-reported PCP use and urinary concentrations of phthalate metabolites and parabens for a large number of men over a 10-y period; these repeated urine samples likely increased the precision of the measurements compared with a single sample. We included in the analysis 14 PCPs, the largest number of PCPs investigated in men to date. We also included a large number of phthalate metabolites and 3 parabens. We assessed PCP use within 24 and 6 h before urine collection, more relevant exposure windows compared with PCP use within the last 48 h (Duty et al. 2005; Just et al. 2010).

Another novel aspect of our study compared with previous research was the use of a weighted PCP score to predict urinary concentrations. The WBS analyses yielded different weights for each PCP depending on the association with each biomarker. Using the single-PCP analyses for modeling, the crude sum of PCPs likely attenuated the effect due to nondifferential misclassification by giving equal weights to each PCP in association with each biomarker. In addition, the multi-PCP approach was able to adjust for the other correlated PCPs and estimate the joint effect of multiple PCP use, rather than simply using the crude sum of the PCPs or modeling each PCP separately. This approach has been used in linear regression settings (Carrico 2013; Carrico et al. 2015; Christensen et al. 2013; Gennings et al. 2010, 2013), and we extended the basic weighted quantile strategy used in earlier work to the repeated measures data and in Tobit regression framework.


Among a large cohort of men, we identified PCP predictors of urinary concentrations of MEP and three parabens. The results will be useful in future exposure assessment studies on PCP use as an important source of exposure to phthalates (mainly DEP) and parabens. Collecting concise PCP use information can be achieved by only asking specific questions about the use of the most relevant PCPs within a narrow exposure window (6 h) to potentially decrease missingness and improve recall decreasing misclassification while also optimizing research cost and time.


The authors gratefully acknowledge the Centers for Disease Control and Prevention (CDC) staff for measuring phthalate metabolites and paraben urinary concentrations, and M.G. Keller, R. Dadd and P. Morey (research staff), the study participants, and the clinical staff for their dedication and participation in the EARTH study. The authors also thank L. Valeri and B. Claus Henn for their useful SAS code that inspired part of this work.

This work was supported by the National Institute of Environmental Health Sciences (NIEHS)/National Institutes of Health (grants R01 ES009718, R01 ES024381, and P30 ES000002). The Leslie Silverman Industrial Hygiene Fund, the Benjamin Greely Ferris, Jr. Fellowship in Environmental Epidemiology, and the Cyprus Endowment for the Environment and Public Health at the Harvard T.H. Chan School of Public Health provided support for F.L.N. during her doctoral studies.

The findings and conclusions in this report are those of the authors and do not necessarily represent the official position of the CDC. Use of trade names is for identification only and does not imply endorsement by the CDC, the Public Health Service, or the U.S. Department of Health and Human Services.


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Association between overall environmental quality and lung cancer survival

Lung cancer remains one of the most prevalent and lethal cancers in the United States. Individual environmental exposures have been associated with lung cancer incidence. However, the impact of cumulative environmental exposures on survival is not well understood. To address this gap, we estimated county level environmental quality in the United States using U.S. Environmental Protection Agency’s (USEPA) Environmental Quality Index (EQI). The EQI captures exposure to over 200 environmental factors across five environmental domains (air, water, land, sociodemographic, and built) for the years 2000-2005. For persons diagnosed with lung cancer from 2000-2005 (n=272,371), individual level data on survival time (through Dec. 31, 2013) and co-variates (age, marital status, sex, histology, stage, race, surgery, radiation) from the Surveillance, Epidemiology, and End Results Program (SEER) were linked to the EQI based on place of residence. We modeled the EQI and domain-specific indices as quartiles (Q; Q4 worst environment) using Cox Proportional Hazard models to estimate hazard ratios (HR) and 95% confidence intervals. We adjusted for individual-level covariates and stratified by stage at diagnosis (local, regional, distant) and rural-urban status. Prior to stratification, overall EQI and individual domains showed mostly null or slight positive or negative associations comparing highest to lowest index quartile (EQIQ4: HR=0.92(0.89,0.95). Post stratification, we observed positive associations for the individual domains across different strata (e.g. Non-Metro Urban, Local Stage AirQ4: HR=1.21(1.11, 1.33); Metro Urban, Local Stage SociodemQ4: HR=1.08(1.03,1.13)). Most positive associations were seen in urban areas and at the localized cancer stage. The results suggest association between poor environmental quality and decreased lung cancer survival, with potential variation by rural-urban status and stage at diagnosis. This abstract does not necessarily reflect EPA policy.

A Reexamination of the Emergy Input to a System from the Wind.

The wind energy absorbed in the global boundary layer (GBL, 900 mb surface) is the basis for calculating the wind emergy input for any system on the Earth’s surface. Estimates of the wind emergy input to a system depend on the amount of wind energy dissipated, which can have a range of magnitudes for a given velocity depending on surface drag and atmospheric stability at the location and time period under study. In this study, we develop a method to consider this complexity in estimating the emergy input to a system from the wind. A new calculation of the transformity of the wind energy dissipated in the GBL (900 mb surface) based on general models of atmospheric circulation in the planetary boundary layer (PBL, 100 mb surface) is presented and expressed on the 12.0E+24 seJ y-1 geobiosphere baseline to complete the information needed to calculate the emergy input from the wind to the GBL of any system. The average transformity of wind energy dissipated in the GBL (below 900 mb) was 1241±650 sej J-1. The analysis showed that the transformity of the wind varies over the course of a year such that summer processes may require a different wind transformity than processes occurring with a winter or annual time boundary.

Modeling a Hydrologically Optimal Green Roof Media Mixture

Background/Questions/MethodsA key environmental concern in managing urban ecosystems is controlling stormwater runoff to ameliorate pollution problems and sewage overflows. Vegetated green roofs have become an important green infrastructure tool to collect, store, and gradually release rainwater over time, with the added benefit of decreasing energy costs by acting as an insulator and increasing albedo. However, a major constraint to the survival of plants on green roofs is the lack of available water, particularly in the Pacific Northwest, where winters are cold and rainy and summers are warm and dry. The hydrologic attributes of the substrate used as growing medium strongly influence water retention, and, thus, plant survival. In this study we developed a simple spreadsheet model to optimize hydrologic performance of green roof media mixtures using data on the hydraulic conductivity {HC}, wet weight {WW}, and water held {WH} at saturation and after 14 days of drying for individual and mixtures of media constituents (peat moss, perlite, pumice, red cinder, sand, vermiculite) typically used in the Pacific Northwest. In addition, the proportion of processed constituents (perlite, vermiculite) was considered as a selection factor. The results of this study are intended to identify optimal green roof media mixtures for specific applications.Results/ConclusionsWe fixed the amount of organic matter (peat moss) at 20% by volume for media mixtures, so variation in hydro logic performance was driven by the composition of the inorganic fraction. Because perlite was light weight and had a high HC, yet held high amounts of water both when saturated and after 14 days, media mixtures dominated by perlite had the best hydrologic characteristics. Pumice also functioned relatively well, but was heavier. Although vermiculite performed very well in the first wetting, its water retention sharply decreased after undergoing a drying and re-wetting cycle, which resulted in an approximate 50% decrease in volume. Mixtures using perlite and/or pumice best addressed the performance criteria. This study demonstrates the potential to design green roofs with an appropriate media to enhance dry season water availability, while optimizing water release. With vegetation adapted to these media and the local environment, green roofs may be more effectively designed to not only mediate runoff, but also assist in cooling buildings and providing habitat.

Nitrogen input inventory in the Nooksack-Abbotsford-Sumas Transboundary Region: Key component of an international nitrogen management study.

Background/Question/Methods: Nitrogen (N) is an essential biological element, so optimizing N use for food production while minimizing the release of N and co-pollutants to the environment is an important challenge. The Nooksack-lower Fraser Valley, spanning a portion of the western interface of British Columbia, Washington state, and the Lummi Nation and the Nooksack Tribe, supports agriculture, fisheries, diverse wildlife, and vibrant urban areas. Groundwater nitrate contamination affects thousands of households in this region. Fisheries and air quality are also affected including periodic closures of shellfish harvest. To reduce the release of N to the environment, successful approaches are needed that partner all stakeholders with appropriate institutions to integrate science, outreach and management efforts. Our goal is to determine the distribution and quantities of N inventories of the watershed. This work synthesizes publicly available data on N sources including deposition, sewage and septic inputs, fertilizer and manure applications, marine-derived N from salmon, and more. The information on cross-boundary N inputs to the landscape will be coupled with stream monitoring data and existing knowledge about N inputs and exports from the watershed to estimate the N residual and inform N management in the search for the environmentally and economically viable and effective solutions. Results/Conclusions: We will estimate the N inputs into the Nooksack-lower Fraser Valley and transfers within the watershed originating from energy use, transportation, fertilization, wastewater treatment plants (WWTP), animal feeding and manure production, and others. Here we present some preliminary results on the U.S. side using available data: About 11% of the watershed is farmed land, and 87% of the production acres in 2015 consisted of forage grass, silage corn, caneberries, blueberries, and potatoes. Using the existing SPAtially Referenced Regression On Watershed attributes (SPARROW) model, we estimated that the manure N input to the watershed from dairy and livestock operations ranged between 17 metric ton (MT) N to 300 MT N in 2002. SPARROW result also indicated that individual WWTP sites contributed between 3 to 220 MT N to the watershed in 2002, and fish hatcheries 0.2 to 28 MT N. We will supplement and update the existing data for more recent years and improve the results by reaching out to local groups to quantify crop-specific N data and animal feed inputs. This project will reach out to other stakeholders on both sides of the international border for a first comprehensive, quantitative characterization of all N inventories across this international watershed.

Air Pollution and Risk of Parkinson’s Disease in a Large Prospective Study of Men

Author Affiliations open

1Department of Public Health, University of Massachusetts, Lowell, Lowell, Massachusetts, USA

2Department of Neurology, John Hopkins Medical Institute, Baltimore, Maryland, USA

3Department of Environmental Health, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA

4Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts, USA

5Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts, USA

6Department of Nutrition, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA

7Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, Massachusetts, USA

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  • Background:
    Exposure to air pollution has been implicated in a number of adverse health outcomes, and the effect of particulate matter (PM) on the brain is beginning to be recognized.
    We aimed to examine whether exposure to PM air pollution is related to risk of Parkinson’s disease (PD) in the Health Professionals Follow-up Study (HPFS), a large prospective cohort of U.S. men.
    We prospectively followed 50,352 men in the HPFS, a large prospective cohort of U.S. men, and identified 550 incident PD cases. Cumulative average exposure to various size fractions of PM [PM10 (≤10 μm microns in diameter), PM2.5 (≤2.5 μm in diameter), and PM2.5–10 (between 2.5 and 10 μm in diameter)] up to 2 years before the onset of PD was estimated using a spatiotemporal model by linking each participant’s place of residence throughout the study with location-specific PM levels. We used multivariable Cox proportional hazards models to independently estimate the risk of PD associated with each size fraction of PM.
    In models adjusted for age, smoking, region, and population density, we did not observe statistically significant associations between exposure to PM and PD risk. In analyses considering cumulative average PM exposure, the comparing the top to the bottom quintile of PM exposure was 0.85 [95% confidence interval (CI): (0.63, 1.15)] for PM10, 0.97 [95% CI: (0.72, 1.32)] for PM2.5, and 0.88 [95% CI: (0.64, 1.22)] for hazard ratio (HR) PM2.5–10. The results did not change markedly when restricted to men who did not move during the study or when stratified by smoking status or population density.
    In this study, we found no evidence that exposure to air pollution is a risk factor for PD in men.
  • Received: 01 April 2016
    Revised: 07 November 2016
    Accepted: 10 November 2016
    Published: 18 August 2017

    Address correspondence to N. Palacios, Dept. of Public Health, University of Massachusetts, Lowell; Lowell, Massachusetts. Telephone: 617-304-4254. Email:

    Statistical analysis was done by N.P. and K.C.F.

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

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Evidence is rapidly growing on the negative health impacts of air pollution (Loomis et al. 2013; Wang et al. 2014). Research into the association between air pollution and neurological disease, particularly Parkinson’s disease (PD), is limited. PD is the second most prevalent neurodegenerative disease, and it results in substantial personal and societal costs (Huse et al. 2005). Toxins in air pollution have been shown to promote inflammation and oxidative stress (Calderon-Garcidueñas et al. 2015), both of which are thought to contribute to PD (Andican et al. 2012). Inflammatory markers have been associated with elevated PD risk in epidemiological studies (Chen et al. 2008; Ton et al. 2012). Furthermore, urate, a strong antioxidant, has been shown to be neuroprotective in animal models (Haberman et al. 2007), and has been associated with reduced PD risk (Davis et al. 1996; de Lau et al. 2005; Weisskopf et al. 2007).

The epidemiologic literature on air pollution and PD is limited, although interest in this topic is growing. Here, we present the results from a large prospective study of U.S. male health professionals, the Health Professionals Follow-up Study (HPFS), where we examined whether exposure to ambient particulate matter (PM), specifically particles smaller or equal to 10 μm in diameter (PM10), particles smaller than 2.5 μm in diameter (PM2.5), and particles between 2.5 and 10 μm in diameter (PM2.5–10), is associated with risk of PD in men.


Study Population

The HPFS cohort (Grobbee et al. 1990) began in 1986, and consists of 51,529 dentists, podiatrists, pharmacists, veterinarians, and optometrists, recruited nationally and located throughout the United States, who were 40 to 75 years of age at the start of the study. The population was selected due to ease of tracking for follow-up, interest in investigating nutritional factors associated with disease, and level of health knowledge. The majority of participants are Caucasian (>91%), reflective of the racial/ethnic composition of the health professional occupations at the time the study began rather than any exclusion criteria established by investigators. The cohort has been followed by mailed questionnaires sent every 2 years to update exposure information and to ascertain nonfatal incident diseases. Response rates are over 93% at each follow-up cycle. The study was approved by an institutional review board at the Harvard School of Public Health, and each participant provided informed consent before the study began.

Exposure Assessment

Monthly exposures to ambient air pollution were estimated for each participant at the appropriate time-matched questionnaire mailing address using spatiotemporal models, discussed in detail elsewhere (Yanosky et al. 2014). In brief, generalized additive mixed models of PM10 from 1988 through 2007 were developed for the continental United States, using monthly average PM10 data from the Environmental Protection Agency (EPA)’s Air Quality System (AQS), a nationwide network of continuous and filter-based monitors, as well as data from various other sources, including the Interagency Monitoring of Protected Visual Environments network, several Harvard-based research studies, and a geographic information system (GIS) for variables such as population density, distance to nearest road, elevation, and urban land use. The estimation of PM2.5 was similar; however, because EPA AQS monitoring data for PM2.5 were not available prior to 1999; separate models were created for pre- and post-1999 PM2.5. To estimate PM2.5 prior to 1999, the model relied on measured PM10 pre-1999 and the PM2.5 to PM10 ratio from the spatiotemporal model post-1999, as well as estimated extinction coefficients from airport visibility data. PM2.5–10 was estimated by subtracting values for PM2.5 from those for PM10. All models showed little bias and a high degree of precision when evaluated with a cross-validation approach, where a subsection of the monitors was held out to compare predicted and observed values (Puett et al. 2011). Monthly exposures to ambient air pollution were estimated for each participant at the appropriate time-matched questionnaire mailing address, and were then summed to form a cumulative average exposure for each participant.

Parkinson’s Disease Ascertainment

The ascertainment of PD cases was conducted as described previously (Ascherio et al. 2001; Gao et al. 2007; Gao et al. 2011). Briefly, in the HPFS, new cases of PD are reported on biennial questionnaires. When a participant reports PD, we then ask for consent to contact the treating neurologist. If consent is provided, we ask the treating neurologist to complete a questionnaire to confirm the PD diagnosis and to send a copy of the participant’s medical record. During follow-up, 1,481 cohort participants self-reported PD; of these, 860 gave permission to view their medical record (the rest were either deceased, denied diagnosis, did not give permission, or we were unable to contact them), and medical records were obtained for 821 participants. The medical records were reviewed, blind to exposure status, by a movement disorder specialist (M.A.S.). A PD case was considered confirmed if the treating physician reported it as either definite or probable, or if the medical record included evidence of either a final diagnosis of PD made by a neurologist or evidence of two or more of the three cardinal signs of PD (bradykinesia, rigidity, rest tremor) in the absence of characteristics suggesting an alternate diagnosis. Additionally, we requested death certificates of all deceased study participants, and identified PD diagnoses that were not reported as part of regular study follow-up (fewer than 2% of all PD cases). Only definite and probable PD cases were used for our analyses, consistent with prior work (Ascherio et al. 2001; Gao et al. 2007).

Statistical Analysis

We used separate time-varying Cox proportional hazards models to model the association between exposure to each fraction of PM and incident PD. The timescale for the left-truncated survival model was age (months), and it is additionally stratified by calendar time in 2-year groups. Person-years of follow-up were accumulated from 1988 through the end of follow-up (30 June 2010), death, or date of PD diagnosis, whichever occurred earlier. We excluded participants who reported PD with onset before the start of follow-up (n=55), participants who died before the start of follow-up (n=412), and participants whose date of birth was not known (n=152). We calculated hazard ratios (HRs) and 95% confidence intervals (CIs) for each quintile of PM exposure, as well as in a linear model for each 10-μg/m3 increase. Quintiles of PM were determined on the initial data set and were the same for all analyses and sensitivity analyses (quintile ranges are listed in Tables 14). For tests of trend, we used the median value of each quintile as a continuous variable to minimize the influence of outliers. Deviations from linearity of continuous PM relationships were assessed with cubic regression splines (Durrleman and Simon 1989). Exposure to air pollution was included in the models as a time-varying variable: every 2 years, a new PM average was calculated as an average of all prior (back to 1988) 2-year PM estimates. The cumulative average of PM exposure was used in an attempt to capture any effects of PM on PD risk prior to onset. Our primary analyses were stratified by age in months and adjusted for calendar year, smoking (never/past/current), region of residence (Northeast, Midwest, West, and South), and median census tract population density (tract level), included in models as time-dependent variables.

Table 1. Age-standardized characteristics of the 50,352 Health Professionals Follow-up Study (HPFS) study participants at baseline in 1988 with respect to quintiles of PM2.5.
Characteristic Q1 Range: 3.1–9.9 μg/m3 Q2 Range: 9.9–13.1 μg/m3 Q3 Range: 13.1–16.3 μg/m3 Q4 Range: 16.3–18.7 μg/m3 Q5 Range: 18.7–29.2 μg/m3
Age, years, mean±SD 56.87±10.15 56.35±9.73 56.55±9.86 56.87±9.70 57.05±9.57
Current smoker % 9.8 9.1 9.5 8.7 9.1
Pack-years, mean±SD 12.1±18.6 12.3±18.9 12.5±19.2 12.2±18.4 11.7±18.1
Caffeine, mg/day, mean±SD 234.2±238.0 231.4±233.7 230.9±225.4 221.1±217.6 216.3±217.2
Alcohol, gm/day, mean±SD 11.7±15.6 12.2±16.3 11.7±15.4 10.7±14.9 10.5±15.0
Census tract incomeb, USD, mean±SD 51,886.8±20,522.4 56,960.6±25,969.7 64,808.6±28,449.9 65,165.3±30,314.9 60,040.2±30,482.4
1988 tract population density (persons/tract),  mean±SD 2,703.9±2,970.8) 3,555.6±4,779.8 3,075.6±3,415.9 4,484.0±6474.6 10,887.0±21,741.1
Region of United States (%)
 Northeast 0.9 8.9 36.4 36.2 35.0
 Midwest 22.1 30.2 36.6 25.7 22.6
 West 44.3 28.7 12.9 5.7 16.9
 South 32.7 32.2 14.2 32.5 25.6

Note: Values are means SD or percentages and are standardized to the age distribution of the study population. Q, quintile; SD, standard deviation.

Table 2. Exposure to cumulate average of PM10, PM2.5, and PM2.5−10 and risk of Parkinson’s disease (PD) in the health professionals follow-up study.
Exposure Person-years Cases Age adjusteda Multivariableb
HR 95% CI HR 95% CI p-trend
Quintiles of PM10
 Q1: 7.4–21.8 μg/m3 187,789 121 1.00 Ref 1.00 Ref
 Q2: 21.8–24.9 μg/m3 190,039 109 0.93 (0.72, 1.21) 0.98 (0.75, 1.28)
 Q3: 24.9–27.7 μg/m3 189,973 112 0.95 (0.74, 1.24) 1.04 (0.79, 1.35)
 Q4: 27.7–31.7 μg/m3 189,573 117 0.95 (0.74, 1.23) 1.04 (0.80, 1.36)
 Q5: 31.7–81.3 μg/m3 188,104 91 0.77 (0.58, 1.01) 0.85 (0.63, 1.15) 0.33
 Linearc 945,478 550 0.98 (0.97, 1.00) 0.99 (0.97, 1.01)
Quintiles PM2.5
 Q1: 3.1–9.9 μg/m3 187,997 98 1.00 Ref 1.00 Ref
 Q2: 9.9–13.1 μg/m3 189,818 114 1.22 (0.93, 1.60) 1.22 (0.92, 1.61)
 Q3: 13.1–16.3 μg/m3 189,880 125 1.29 (0.98, 1.68) 1.24 (0.93, 1.64)
 Q4: 16.3–18.7 μg/m3 189,949 119 1.23 (0.94, 1.60) 1.21 (0.91, 1.62)
 Q5: 18.7–29.2 μg/m3 187,836 94 0.96 (0.72-1.27) 0.97 (0.72, 1.32) 0.61
 Linearc 945,478 550 0.99 (0.97, 1.02) 0.99 (0.97, 1.02)
Quintiles of PM2.5−10
 Q1: 1.6–7.9 μg/m3 188,998 133 1.00 (Ref) 1.00 (Ref)
 Q2: 7.9–10.4 μg/m3 190,395 110 0.79 (0.61, 1.02) 0.84 (0.65, 1.09)
 Q3: 10.4–13.0 μg/m3 188,336 112 0.80 (0.62, 1.03) 0.89 (0.68, 1.16)
 Q4: 13.0–16.8 μg/m3 188,815 93 0.69 (0.53, 0.90) 0.78 (0.58, 1.04)
 Q5: 16.8–59.7 μg/m3 188,935 102 0.77 (0.60-1.00) 0.88 (0.64, 1.22) 0.57
 Linearc 945,478 550 0.98 (0.97, 0.99) 0.99 (0.96, 1.01)

Note: CI, confidence interval; HR, hazard ratio; PM, particulate matter; PY, person-years; Ref, reference.

aadjusted for age and time period.

badjusted for age, time period, smoking (status and pack-years), region (Northeast, Midwest, West, and South) and census tract population density.

cLinear models represent HR per 10-μg/m3 increase in PM.

Table 3. Exposure to PM10, PM2.5, and PM2.5–10 in the year 2000 and risk of Parkinson’s disease (PD) in the health professionals follow-up study.
Exposure Person-years Cases Age adjusteda Multivariableb
HR 95% CI HR 95% CI p-trend
Quintiles of PM10
 Q1: 7.4–21.8 μg/m3 190,377 126 1.00 Ref 1.00 Ref
 Q2: 21.8–24.9 μg/m3 188,529 127 1.01 (0.79, 1.30) 1.06 (0.82, 1.38)
 Q3: 24.9–27.7 μg/m3 189,113 115 0.94 (0.73, 1.21) 1.00 (0.76, 1.31)
 Q4: 27.7–31.7 μg/m3 189,374 93 0.74 (0.57, 0.97) 0.81 (0.60, 1.08)
 Q5: 31.7–81.3 μg/m3 186,970 89 0.74 (0.56, 0.97) 0.83 (0.62, 1.12) 0.07
 Linearc 923,899 550 0.97 (0.96, 0.99) 0.98 (0.96, 0.99)
Quintiles PM2.5
 Q1: 3.1–9.9 μg/m3 187,550 110 1.00 Ref 1.00 Ref
 Q2: 9.9–13.1 μg/m3 190,954 133 1.24 (0.96, 1.60) 1.22 (0.94, 1.60)
 Q3: 13.1–16.3 μg/m3 187,141 106 1.02 (0.78, 1.33) 1.01 (0.75, 1.35)
 Q4: 16.3–18.7 μg/m3 188,643 115 1.06 (0.82, 1.38) 1.08 (0.81, 1.44)
 Q5: 18.7–29.2 μg/m3 188,074 86 0.83 (0.63, 1.11) 0.88 (0.65, 1.19) 0.25
 Linearc 944,362 550 0.97 (0.95, 1.00) 0.98 (0.95, 1.00)
Quintiles of PM2.5–10
 Q1: 1.6–7.9 μg/m3 189,550 127 1.00 Ref 1.00 Ref
 Q2: 7.9–10.4 μg/m3 190,954 107 0.83 (0.64, 1.08) 0.88 (0.68, 1.14)
 Q3: 10.4–13.0 μg/m3 187,141 111 0.82 (0.64, 1.07) 0.89 (0.68, 1.16)
 Q4: 13.0–16.8 μg/m3 188,643 104 0.79 (0.61, 1.03) 0.89 (0.68, 1.16)
 Q5: 16.8–59.7 μg/m3 188,074 101 0.81 (0.62, 1.05) 0.92 (0.68, 1.18) 0.68
 Linearc 923,899 550 0.97 (0.95, 0.99) 0.98 (0.96, 1.00)

Note: CI, confidence interval; HR, hazard ratio; PM, particulate matter; PY, person-years; Ref, reference.

aadjusted for age and time period.

badjusted for age, time period, smoking (status and pack-years), region (Northeast, Midwest, West, and South) and census tract population density.

cLinear models represent HR per 10-μg/m3 increase in PM.

Table 4. Exposure to particulate matter (PM) and risk of Parkinson’s disease (PD) in the health professionals follow-up study, stratified by smoking status.
Exposure Never Smokers Ever Smokers p-int
PY Cases HRa 95% CI PY Cases HRa 95% CI
Quintiles PM10
 7.4–21.8 μg/m3 92,609 62 1.00 Ref 103,446 61 1.00 Ref
 21.8–24.9 μg/m3 93,454 64 1.11 (0.77, 1.59) 105,880 50 0.88 (0.60, 1.29)
 24.9–27.7 μg/m3 93,601 58 1.05 (0.72, 1.53) 105,526 58 1.02 (0.70, 1.48)
 27.7–31.7 μg/m3 93,427 67 1.17 (0.81, 1.70) 104,968 54 0.92 (0.62, 1.36)
 31.7–81.3 μg/m3 98,692 57 0.95 (0.63, 1.42) 99,965 39 0.78 (0.50, 1.21) 0.47
Quintiles PM2.5
 3.1–9.9 μg/m3 95,009 52 1.00 Ref 101,526 47 1.00 Ref
 9.9–13.1 μg/m3 9,5637 62 1.29 (0.88, 1.89) 103,402 55 1.10 (0.74, 1.65)
 13.1–16.3 μg/m3 93,935 70 1.32 (0.89, 1.94) 104,798 57 1.08 (0.72, 1.64)
 16.3–18.7 μg/m3 92,795 72 1.40 (0.95, 2.08) 106,348 57 1.07 (0.70, 1.63)
 18.7–29.2 μg/m3 94,406 52 0.98 (0.65, 1.49) 103,711 46 0.91 (0.58, 1.41) 0.78
Quintiles PM2.5–10
 1.6–7.9 μg/m3 93,485 74 1.00 Ref 104,476 65 1.00 Ref
 0.9–10.4 μg/m3 91,695 64 0.77 (0.55, 1.09) 107,611 51 0.79 (0.54, 1.15)
 10.4–13.0 μg/m3 90,836 55 0.77 (0.51, 1.15) 106,781 61 0.92 (0.63 1.33)
 13.0–16.8 μg/m3 93,763 46 0.80 (0.57, 1.12) 10,3928 47 0.80 (0.53, 1.21)
 16.8–59.7 μg/m3 102,004 69 0.86 (0.62, 1.18) 96,988 38 0.77 (0.47, 1.25) 0.65

Note: CI, confidence interval; HR, hazard ratio; PM, particulate matter; PY, person-years; Ref, reference.

aadjusted for age, time period, smoking (status and pack-years), region (Northeast, Midwest, West, and South) and census tract population density.

Because the relevant etiologic period of air pollution exposure in PD is unknown, we conducted additional sensitivity analyses using PM levels in 2000 (the midpoint of the study) as the exposure of interest. Also, as longer cumulative exposure may be more important, we conducted additional sensitivity analyses among participants with 10 or more years of PM exposure data. We also conducted additional sensitivity analyses adding caffeine intake (<100 mg/day vs. over 100 mg/day) and median census tract income to the models. Additionally, we conducted sensitivity analyses restricted to men who did not move during the study (a move was defined as a change in latitude/longitude of greater than 5 kilometers during the study).

Because smoking is a well-known protective factor in PD epidemiology and may have modes of action that mimic air pollution, we conducted analyses stratified by smoking status (never/ever). We also conducted analyses stratified by tertile of census tract–level population density.

We used SAS version 9.4 (SAS Institute Inc., Cary, NC, USA) for all statistical analyses.


The characteristics of the study population by quintile of PM2.5 are presented in Table 1; characteristics for PM10 and PM2.5–10 were similar. Our study included 50,352 participants, and 550 PD cases were identified throughout follow-up. PM2.5 exposure was not associated with age, smoking status, caffeine or alcohol intake, or census tract income. As expected, participants living in census tracts with higher population density had higher PM exposure.

In models adjusted for age, time period, smoking, region, and population density, we observed no statistically significant associations between exposure to air pollution and PD risk (Table 2). The HR comparing the top to the bottom quintile of PM exposure was 0.85 (95% CI: 0.63, 1.15) for PM10, 0.97 (95% CI: 0.72, 1.32) for PM2.5, and 0.88 (95% CI: 0.64, 1.22) for PM2.5–10. Likewise, in linear models calculating the association between each 10-μg/m3 increase in PM, there was no association observed with PD [HR=0.99 (95% CI: 0.97, 1.01)] for PM10, 0.99 (95% CI: 0.97, 1.02) for PM2.5, and 0.99 (95% CI: 0.96, 1.01) for PM2.5–10. We used splines to test for linearity, and there was some evidence of a nonlinear relation for PM2.5–10 (test for curvature p=0.04), although the test for overall significance of the dose–response was not statistically significant (p=0.13). For PM10 and PM2.5, there was no evidence of deviations from linearity.

Results of sensitivity analyses using PM levels in 2000 as the exposure of were similar to the main results, and are presented in Table 3. Sensitivity analyses among participants with 10 or more years of PM exposure data also did not differ significantly from the main results, although the association, especially for PM10 and PM2.5, tended somewhat more in the direction of a “protective” effect of air pollution on PD risk. However, because none of the associations observed in this sensitivity analysis were strongly protective or statistically significant, they still contribute to the overall finding of no association between air pollution exposure and PD risk in this study. In sensitivity analyses where the models additionally included caffeine intake (<100 mg/day vs. over 100 mg/day) and median census tract, the results did not differ substantially from the main results. In analyses restricted to men who did not move throughout the study (427 PD cases), our results were not significantly different from our main analyses, and we did not observe any association with PD (Table 5).

Table 5. Exposure to PM10, PM2.5, and PM2.5–10 and risk of Parkinson’s disease (PD) in the health professionals follow-up study among nonmovers.
Exposure PY Cases Multivariablea
HR 95% CI p-trend
Quintiles of PM10
 7.4–21.8 μg/m3 149,434 96 1.00 Ref
 21.8–24.9 μg/m3 146,735 81 0.98 (0.72, 1.33)
 24.9–27.7 μg/m3 148,683 80 0.98 (0.72, 1.34)
 27.7–31.7 μg/m3 150,814 99 1.14 (0.85, 1.54)
 31.7–81.3 μg/m3 153,144 71 0.88 (0.62, 1.24) 0.62
Quintiles of PM2.5
 3.1–9.9 μg/m3 146,665 84 1.00 Ref
 9.9–13.1 μg/m3 145,982 87 1.14 (0.84, 1.56)
 13.1–16.3 μg/m3 147,473 97 1.16 (0.84, 1.60)
 16.3–18.7 μg/m3 151,200 82 0.96 (0.69, 1.34)
 18.7–29.2 μg/m3 157,492 77 0.89 (0.63, 1.25) 0.18
Quintiles of PM2.5–10
 1.6–7.9 μg/m3 155,118 95 1.00 Ref
 7.9–10.4 μg/m3 150,279 86 1.01 (0.75, 1.37)
 10.4–13.0 μg/m3 144,904 86 1.10 (0.80, 1.50)
 13.0–16.8 μg/m3 147,449 76 1.00 (0.71, 1.39)
 16.8–59.7 μg/m3 151,062 84 1.21 (0.85, 1.73) 0.27

Note: Nonmovers were defined as participants whose address changed by less than 5 km during the study. CI, confidence interval; HR, hazard ratio; PM, particulate matter; PY, person-years; Ref, reference.

aAdjusted for age, time period, smoking (status and pack-years), region (Northeast, Midwest, West, and South) and census tract population density.

We did not observe any statistically significant interactions with smoking status (Table 4). When we stratified our analyses by population density, we saw no evidence for effect modification by population density (Figure 1).

Three panels of line graphs with confidence intervals.
Figure 1. Association between particulate matter (PM) and Parkinson’s disease (PD) risk, stratified by tertile of tract-level population density. Hazard ratio per linear 10-μg/m3 increase PM10, PM2.5, and PM2.5–10 by tract-level population density tertile (low, middle, high) adjusted for age, time period, smoking (status and pack-years), and region (Northeast, Midwest, West, and South).


In this study, we did not observe an association between exposure to ambient air pollution measured as cumulative exposure to PM10, PM2.5, and PM2.5–10 at the participant’s mailing address and risk of PD in a U.S.–based study of men. In analyses considering cumulative average PM exposure, the HR comparing the top to the bottom quintile of PM exposure was 0.85 (95% CI: 0.63, 1.15) for PM10, 0.97 (95% CI: 0.72, 1.32) for PM2.5, and 0.88 (95% CI: 0.64, 1.22) for PM2.5–10. These findings were similar to those we have previously published on exposure to PM and PD risk in the Nurses’ Health Study (NHS), a similarly designed study of women (Palacios et al. 2014a).

PM10 describes all particles that are smaller than 10 μm in diameter, these particles are considered small enough to pass through the throat or the nose and enter the lungs, thus potentially causing harm to human health. (PM). Thus, PM10 encompasses both inhalable coarse particles (PM2.5–10) and fine particles (PM2.5). Inhalable coarse particles, PM2.5–10, come primarily from agricultural, mining and construction sources (Kagan and Maddison 1992). Fine particles, PM2.5, are mostly combustion-derived particles and are produced from the burning of coal, wood and fuel oil and from motor vehicle emissions.

The potential effects of air pollution on neurological disease broadly and PD in particular are just beginning to be understood. Finkelstein and colleagues (Finkelstein and Jerrett 2007) found that although markers of traffic derived air pollution did not predict PD risk, risk was increased among participants with higher manganese (Mn) exposure (HR:1.03; 95% CI:1.00–1.07) for each 10-ng/m3 increase in Mn concentration) (Finkelstein and Jerrett 2007). Willis and colleagues linked airborne metal exposure data throughout the United States to the Medicare beneficiaries database, and reported that incidence of PD was elevated in urban counties with higher industrial release of both copper and manganese (Willis et al. 2010). Our group recently published on the association between airborne metals and risk of PD in the NHS, a similarly designed study of women (Palacios et al. 2014b), showing a potential association with airborne tract–level mercury exposure and PD risk in women (Palacios et al. 2014a). In a separate paper, we also reported a lack of an association between PM air pollution and PD risk in the NHS cohort (Palacios et al. 2014a), a cohort of women with similar design to the present study. In a recent study, Ritz et al. (2016) found that modeled ambient air pollution from traffic sources (particularly NO2) was associated with a 9% increase in PD risk per interquartile range, with some evidence of effect modification by urbanicity: the odds ratio (OR) associated with NO2 exposure for long-term residents of the capital city (Copenhagen, Denmark) was higher (OR=1.21; 95% CI: 1.11–1.31) than that in provincial towns (OR=1.10; 95% CI: 0.97–1.26), and no association was seen among residents of rural areas. Our finding of a lack of effect modification by population density is not consistent with this finding. One possible reason for this is that Ritz et al. (2016) focused on pollution from traffic sources (particular NO2), while our study examines PM, which is a complex mixture and represents other components in addition to traffic air pollution. A recently published study by Liu et al. (2016) examined PM10, PM2.5, and NO2 relation to PD risk within the NIH–AARP Diet and Health Study. The main finding from that work is of no association between PM air pollution and PD, and it is in agreement with our results. However, in subgroup analyses, Liu et al. (2016) reported significant positive associations between PM10 among women and moderate evidence for an increased risk associated with exposure PM2.5 among smokers. As our study included only men, it is in agreement with Liu et al. (2016) as to no association between exposure to PM air pollution and PD risk in men. We also conduced analyses stratified by smoking, one of the best-known protective factors for PD (Hernán et al. 2001; Morozova et al. 2008; O’Reilly et al. 2009). In our study, smoking did not appear to modify the lack of association between air pollution and risk of PD, which is somewhat contradictory to the findings by Liu et al. (2016). However, in the study by Liu et al. (2016), the finding of an increased risk among never smokers is of marginal significance, is not consistent across quintiles or in the linear model, and may be due to chance. More work on understanding the impact of exposure to air pollution among specific subgroups, such as nonsmokers, is needed.

A major strength of our study is the careful ascertainment of PD in the HPFS cohort. The ascertainment of PD cases was conducted as described in “Methods” and in previous studies (Ascherio et al. 2001; Gao et al. 2007; Gao et al. 2011), and included contacting the treating neurologist for each patient self-reporting PD, as well as confirmation by a study neurologist (M.A.S.) of the information reported by the patient’s treating neurologist. However, it is still possible that PD cases, due to their onset of ill health, might be somewhat less likely to complete questionnaires. To address this, we examined death certificates for all study participants, and ascertained PD cases after death. Fewer than 2% of PD cases in this study were ascertained after death. Thus, the large majority (over 98%) of PD cases were reported in life, indicating that selective nonresponse due to PD, if present, would have a minimal impact on the study.

Another advantage of our study is that the exposure models are based on a comprehensive prediction model of PM, which allowed us to estimate air pollution levels for an entire nonoccupationally exposed cohort based on participants’ questionnaire mailing addresses. Our air pollution models take advantage of GIS-based spatiotemporal statistics with GIS covariates, and allow us to account for small-scale variation in pollution exposure around each study participant’s address. Additionally, our biennial collection of addresses allows us to estimate pollution levels at each address where each study participant resided during follow-up.

The primary challenge of this study was the measurement of long-term exposure of air pollution. Our study is based on modeled estimates of ambient air pollution exposure in a large population of U.S. men. We did not have personal air pollution measurements or indoor air pollution measures for our study participants, and we did not know how much time they spent indoors vs. outdoors at their address. The use of ambient outdoor PM exposure could have attenuated our estimates towards the null compared to estimates that would have been expected with personal PM monitoring (Kioumourtzoglou et al. 2014). However, the exposure of interest in this study is outdoor PM and not personal PM exposure, and PM is regulated based on its outdoor ambient levels; we believe that outdoor ambient PM is an appropriate metric for this study. Furthermore, the addresses provided in the HPFS were either business or residential addresses, and, with the exception of 1988, when type of address was asked of the participants, we did not have information on which of the two were provided. The models used to estimate air pollution exposure have been shown to have little bias and high precision (Hart et al. 2015). However, some misclassification of the biologically relevant levels of individual exposure is inevitable, and could have attenuated our estimate of the association between air pollution and PD risk toward the null. We were also not able to account for occupational exposure to air pollution (if any), or other potential neurotoxins among our study participants, but we do not expect significant on-the-job exposure to air pollution given the occupational nature of this cohort. Exposure to neurotoxins, if any, would have to be correlated with exposure to ambient air pollution, which is not likely. Also, because this study was based in the United States, air pollution levels studied here are lower than those experienced by people in other, more polluted areas of the world.

Furthermore, the most relevant exposure window that needs to be considered in order to detect any potential association between pollution (or any other toxin) and PD is currently unknown. A recent study of smoking, the exposure with the best documented link to this disease, and PD showed smoking up to 20 years prior to onset was associated with PD risk, and the most recent exposure (up to or nearly up to the onset of symptoms) was the most strongly associated with risk of PD (Thacker et al. 2007). Because we only had residential history starting in 1986 (the inception of the cohort) and our air pollution models only started in 1988 due to the availability of monitoring data, we were only able to estimate exposure during adulthood for our study participants. Although this has not been determined, it is possible that the relevant etiological period may be much earlier in life, including childhood, or for a longer period than we were able to capture in this study. The analyses of men who did not move during the study attempt to address this concern, making the assumption that these men were also more likely to maintain a single address prior to study baseline than those who had moved during the study. Furthermore, because the study participants were highly educated and primarily Caucasian male health professionals of relatively high socioeconomic status, the results of this study may not be generalizable to the wider United States and world population.

The advantages of this study include a prospective design, large size, and a high follow-up rate. The study also benefited from rigorous follow-up for PD. The study participants’ addresses, and thus the modeled air pollution estimates, were located throughout the continental United States, giving us a wide range in levels of air pollution exposure for our study.


In summary, overall the results of this large cohort study of male health professionals do not support an effect of air pollution on PD risk in men.


The authors would like to acknowledge L. Unger for administrative support and Dr. E. O’Reilly for statistical advice.

The study was supported by National Institutes of Health (NIH) K01 ES019183, UM1 CA167552, R01 ES017017, and P30 ES000002.


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It’s a Secret!

Have you ever heard the expression “as secure as Fort Knox?” It means something is very well-protected from intruders. Maybe you already know that Fort Knox is a real place in Kentucky. But did you know the real Fort Knox…

USAID Recognizes World Humanitarian Day on August 19

Friday, August 18, 2017

This Saturday, August 19, marks World Humanitarian Day, a time to recognize aid workers who risk their lives while helping millions of people affected by global crises. Since the death of 22 United Nations and relief-agency staff on August 19, 2003 in a Baghdad bombing, each year we honor the brave women and men who have died while serving others, and celebrate the selfless spirit of those who risk everything to save lives.

Model application niche analysis: An approach for assessing the transferability and generalizability of ecological models

A 30-year review of predictive models used in regulatory decision-making, revealed that transferring models to contexts other than that for which the models were developed was one of the biggest vulnerabilities to their legal defensibility. The use and transfer of models by ecologists, to inform environmental management and decision-making, has grown exponentially in the past 50 years. Given this trend, and the importance of public confidence in the decisions that are being made based on models, model users need better ways to evaluate the possibility of misuse when transferring models to new contexts. We present one approach, a model application niche analysis, where ecologists synthesize information from databases and past studies to create model performance curves and decision landscape plots. These visualization tools characterize a model’s application niche as a function of model performance and uncertainty across dimensions of context, as a means to evaluate both model transferability and generalizability.
To demonstrate the utility of this approach, we evaluated an empirical model developed to predict the mean coefficient of conservatism (i.e., ecological condition) of plant communities in wetlands across Pennsylvania for transfers across the contiguous U.S. The model predictors include surface soil pH and the landscape development index, a measure of anthropogenic activity in the wetland’s surrounding landscape. Using model performance curves and decision landscape plots along latitude and elevation gradients, we show that this model is transferable to locations in the eastern, southeastern and Pacific Northwest regions of the U.S. where high forest cover, rainfall and geology contribute to acidic soil formation. We also show that model performance is weakest, and the model structure is questionable, in regions of the U.S. with basic soil conditions, such as the west and the tips of Florida and Louisiana. Our goal is to invoke further inquiries into the development of consistent and transparent practices for model selection when transferring ecological models.

NEP-ORD Collaborations on Quantifying the Ecosystem Goods and Services of Estuaries

Nature’s goods and services provided by estuarine ecosystems are integral to the economies and well-being of coastal communities. However, many valuable ecosystem goods and services (EGS), including their benefits to surrounding communities, are not explicitly considered in decisions concerning the management or uses of estuarine habitats and associated watersheds. Projects in ORD’s Sustainable and Health Communities National Research Program (SHC) are focused on developing and testing methods to quantify EGS, predict their sensitivities to natural and human stressors, and link them to human well-being, ultimately facilitating their inclusion in decision-making. This presentation will summarize research conducted under SHC projects, in collaboration with National Estuary Program (NEP) partners in Tampa Bay (FL) and Tillamook Bay (OR), to measure and model estuarine and watershed EGS and related benefits. While each collaboration was developed independently, they share a common goal of providing the NEPs with data and tools conveying the benefits of EGS. These resources can then help inform discussions with stakeholders about achieving (or maintaining) desired future conditions and functions of coastal ecosystems within the boundaries of each NEP. ORD research leaders (Russell, DeWitt) will present overviews of the EGS research conducted in Tampa Bay and Tillamook Bay (respectively), complimented by presentations by Executive Directors (Greening and Phipps, respectively) on how the resulting information and tools are being used by each NEP. Webinar attendees will be encouraged to participate in a discussion exploring how EGS assessments can be useful in helping to implement CCMP objectives.

Atlas V Rocket and TDRS-M

As the Sun rises at Space Launch Complex 41 at Cape Canaveral Air Force Station in Florida, a United Launch Alliance Atlas V rocket vents liquid oxygen propellant vapors during fueling for the lift off of NASA’s Tracking and Data Relay Satellite, TDRS-M.