The impact of an anti-idling campaign on outdoor air quality at four urban schools.
Patrick H. Ryan,*Tiina Reponen, Mark Simmons, Michael Yermakov, Ken Sharkey, Denisha Garland-Porter, Cynthia Eghbalniad and Sergey A. Grinshpunb
Courtesy RSC Publishing.
1January 2020, Volume 8, Issue 1, Number 17Maryam Bahr.docxaulasnilda
1
January 2020, Volume 8, Issue 1, Number 17
Maryam Bahreynian1 , Marjan Mansourian2 , Nafiseh Mozaffarian3 , Parinaz Poursafa4 , Mehri Khoshhali3* , Roya Kelishadi3
Review Article:
The Association Between Exposure to Ambient Particulate
Matter and Childhood Obesity: A Systematic Review and
Meta-analysis
Context: Physical environment contamination and in particular, air pollution might cause long-term
adverse effects in child growth and a higher risk of catching non-communicable diseases later in life.
Objective: This study aimed to overview the human studies on the association of exposure to
ambient Particulate Matter (PM) with childhood obesity.
Data Sources: We systematically searched human studies published until March 2018 in PubMed,
Scopus, Ovid, ISI Web of Science, Cochrane library, and Google Scholar databases.
Study Selection: All studies that explored the association between PM exposure and childhood
obesity were assessed in the present study, and finally, 5 studies were used in the meta-analysis.
Data Extraction: Two independent researchers performed the data extraction procedure and
quality assessment of the studies. The papers were qualitatively assessed by STROBE (Strengthening
the Reporting of Observational studies in Epidemiology) statement checklist.
Results: The pooled analysis of PM exposure was significantly associated with increased Body Mass
Index (BMI) (Fisher’s z-distribution=0. 028; 95% CI=0. 017, 0. 038) using the fixed effects model. We
also used a random-effect model because we found a significant high heterogeneity of the included
studies concerning the PM (I2=94. 4%; P<0. 001). PM exposure was associated with increased BMI
(Fisher’s z-distribution=0. 022; 95% CI=-0. 057, 0. 102). However, the overall effect size was not
significant, and heterogeneity of the included studies was similar to the fixed effect model.
Discussion: Our findings on the significant association between PM10 exposure and the increased
BMI (r=0. 034; 95%CI=0. 007, 0. 061) without heterogeneity (I2=16. 6%, P=0. 274) (in the studies with
PM10) suggest that the PM type might account for the heterogeneity among the studies.
Conclusion: The findings indicate that exposure to ambient PM10 might have significant effects on
childhood obesity.
A B S T R A C T
Key Words:
Air pollution, Particulate
matter, Childhood
obesity, Meta-analysis
Article info:
Received: 10 Oct 2018
First Revision: 23 Feb 2019
Accepted: 09 Mar 2019
Published: 01 Jan 2020
1. Department of Nutrition Child Growth, and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Dis-
eases, Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran.
2. Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
3. Department of Pediatrics, Child Growth, and Development Research Center, Research Institute for Primordial Preve ...
1January 2020, Volume 8, Issue 1, Number 17Maryam Bahr.docxjesusamckone
1
January 2020, Volume 8, Issue 1, Number 17
Maryam Bahreynian1 , Marjan Mansourian2 , Nafiseh Mozaffarian3 , Parinaz Poursafa4 , Mehri Khoshhali3* , Roya Kelishadi3
Review Article:
The Association Between Exposure to Ambient Particulate
Matter and Childhood Obesity: A Systematic Review and
Meta-analysis
Context: Physical environment contamination and in particular, air pollution might cause long-term
adverse effects in child growth and a higher risk of catching non-communicable diseases later in life.
Objective: This study aimed to overview the human studies on the association of exposure to
ambient Particulate Matter (PM) with childhood obesity.
Data Sources: We systematically searched human studies published until March 2018 in PubMed,
Scopus, Ovid, ISI Web of Science, Cochrane library, and Google Scholar databases.
Study Selection: All studies that explored the association between PM exposure and childhood
obesity were assessed in the present study, and finally, 5 studies were used in the meta-analysis.
Data Extraction: Two independent researchers performed the data extraction procedure and
quality assessment of the studies. The papers were qualitatively assessed by STROBE (Strengthening
the Reporting of Observational studies in Epidemiology) statement checklist.
Results: The pooled analysis of PM exposure was significantly associated with increased Body Mass
Index (BMI) (Fisher’s z-distribution=0. 028; 95% CI=0. 017, 0. 038) using the fixed effects model. We
also used a random-effect model because we found a significant high heterogeneity of the included
studies concerning the PM (I2=94. 4%; P<0. 001). PM exposure was associated with increased BMI
(Fisher’s z-distribution=0. 022; 95% CI=-0. 057, 0. 102). However, the overall effect size was not
significant, and heterogeneity of the included studies was similar to the fixed effect model.
Discussion: Our findings on the significant association between PM10 exposure and the increased
BMI (r=0. 034; 95%CI=0. 007, 0. 061) without heterogeneity (I2=16. 6%, P=0. 274) (in the studies with
PM10) suggest that the PM type might account for the heterogeneity among the studies.
Conclusion: The findings indicate that exposure to ambient PM10 might have significant effects on
childhood obesity.
A B S T R A C T
Key Words:
Air pollution, Particulate
matter, Childhood
obesity, Meta-analysis
Article info:
Received: 10 Oct 2018
First Revision: 23 Feb 2019
Accepted: 09 Mar 2019
Published: 01 Jan 2020
1. Department of Nutrition Child Growth, and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Dis-
eases, Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran.
2. Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
3. Department of Pediatrics, Child Growth, and Development Research Center, Research Institute for Primordial Preve.
Designing a Low-Cost Pollution Prevention Plan to Pay Off at.docxcuddietheresa
Designing a Low-Cost Pollution Prevention Plan to Pay Off at
the University of Houston
Yurika Diaz Bialowas, Emmett C. Sullivan, and Robert D. Schneller
Environmental Health and Risk Management Department, University of Houston, Houston, TX
ABSTRACT
The University of Houston is located just south of down-
town Houston, TX. Many different chemical substances
are used in scientific research and teaching activities
throughout the campus. These activities generate a signif-
icant amount of waste materials that must be discarded as
regulated hazardous waste per U.S. Environmental Protec-
tion Agency (EPA) rules. The Texas Commission on Envi-
ronmental Quality (TCEQ) is the state regulatory agency
that has enforcement authority for EPA hazardous waste
rules in Texas. Currently, the University is classified as a
large quantity generator and generates �1000 kg per
month of hazardous waste. In addition, the University
has experienced a major surge in research activities during
the past several years, and overall the quantity of the
hazardous waste generated has increased. The TCEQ re-
quires large quantity generators to prepare a 5-yr Pollu-
tion Prevention (P2) Plan, which describes efforts to elim-
inate or minimize the amount of hazardous waste
generated. This paper addresses the design and develop-
ment of a low-cost P2 plan with minimal implementation
obstacles and strong payoff potentials for the University.
The projects identified can be implemented with existing
University staff resources. This benefits the University by
enhancing its environmental compliance efforts, and the
disposal cost savings can be used for other purposes.
Other educational institutions may benefit by undertak-
ing a similar process.
INTRODUCTION
The University of Houston campus covers �550 acres and
includes �100 buildings. Current enrollment is �35,000
students with �5000 faculty and staff.1 There are �400
laboratories on campus presently with more planned in
2006.
Colleges and universities typically generate a wide
range of chemical waste and, because of their decentral-
ized organizational structure face, challenges in comply-
ing with applicable waste regulations.2,3 One of the criti-
cal functions of the University’s Environmental Health
and Risk Management Department (EHRM) is to manage
chemical waste in accordance with the U.S. Environmen-
tal Protection Agency (EPA) and Texas Commission on
Environmental Quality (TCEQ) rules.4,5 This is a sizable
undertaking for the EHRM staff and ties up many re-
sources. There is no formal long-term investment in waste
disposal costs for the University other than a demonstra-
tion of regulatory compliance.6 Therefore, it is in the best
interest of the University to minimize the quantity of
chemical waste generated whenever possible so that funds
could be spent on projects other than waste disposal.
In addition, the TCEQ has promulgated rules that
require large-quantity hazardous waste generators, such
as the University, t ...
Identifying and Prioritizing Chemicals with Uncertain Burden oMalikPinckney86
Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure:
Opportunities for Biomonitoring and Health-Related Research
Edo D. Pellizzari,1 Tracey J. Woodruff,2 Rebecca R. Boyles,3 Kurunthachalam Kannan,4 Paloma I. Beamer,5 Jessie P. Buckley,6
Aolin Wang,2 Yeyi Zhu,7,8 and Deborah H. Bennett9 (Environmental influences on Child Health Outcomes)
1Fellow Program, RTI International, Research Triangle Park, North Carolina, USA
2Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San
Francisco, San Francisco, California, USA
3Bioinformatics and Data Science, RTI International, Research Triangle Park, North Carolina, USA
4Wadsworth Center, New York State Department of Health, Albany, New York, USA
5Department of Community, Environment and Policy, Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA
6Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Heath, Johns Hopkins University,
Baltimore, Maryland, USA
7Northern California Division of Research, Kaiser Permanente, Oakland, California, USA
8Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
9Department of Public Health Sciences, University of California, Davis, Davis, California, USA
BACKGROUND: The National Institutes of Health’s Environmental influences on Child Health Outcomes (ECHO) initiative aims to understand the
impact of environmental factors on childhood disease. Over 40,000 chemicals are approved for commercial use. The challenge is to prioritize chemi-
cals for biomonitoring that may present health risk concerns.
OBJECTIVES: Our aim was to prioritize chemicals that may elicit child health effects of interest to ECHO but that have not been biomonitored nation-
wide and to identify gaps needing additional research.
METHODS: We searched databases and the literature for chemicals in environmental media and in consumer products that were potentially toxic. We
selected chemicals that were not measured in the National Health and Nutrition Examination Survey. From over 700 chemicals, we chose 155 chemi-
cals and created eight chemical panels. For each chemical, we compiled biomonitoring and toxicity data, U.S. Environmental Protection Agency ex-
posure predictions, and annual production usage. We also applied predictive modeling to estimate toxicity. Using these data, we recommended
chemicals either for biomonitoring, to be deferred pending additional data, or as low priority for biomonitoring.
RESULTS: For the 155 chemicals, 97 were measured in food or water, 67 in air or house dust, and 52 in biospecimens. We found in vivo endocrine, de-
velopmental, reproductive, and neurotoxic effects for 61, 74, 47, and 32 chemicals, respectively. Eighty-six had data from high-throughput in vitro
assays. Positive results for endocrine, developmental, neurotoxicity, ...
Why is it so hard to reduce household air pollution among the very poor?Leith Greenslade
What cooking technologies can deliver lasting reductions in exposure to household air pollution among the very poor? This is THE question. Learn more from four experts, including Neil Schluger and Darby Jack (Columbia University), Alison Lee (Icahn School of Medicine Mt Sinai) and Joshua Rosenthal (NIH), on the latest research and the most promising technologies, especially the new efforts to reroute government fuel subsidies from the middle class to the very poor (e.g. India Give it Up Campaign for LPG).
Dr. Fred C. Lunenburg[1]. environmental hazards in america's schools focus v4...William Kritsonis
Dr. Fred C. Lunenburg, www.nationalforum.com, Dr. William Allan Kritsonis, Editor-in-Chief, National FORUM Journals, Houston, Texas
www.nationalforum.com
By age 18, the lungs of many children who grow up in smoggy ar.docxRAHUL126667
By age 18, the lungs of many children who grow up in smoggy areas are underdeveloped and will likely
never recover, according to a study by Keck School of Medicine researchers in the New England Journal of
Medicine.
Health [/category/health/]
USC study links smoggy air to
lung damage in children
BY Alicia Di Rado [/author/alicia-di-rado/] SEPTEMBER 17, 2004
The research is part of the Children’s Health Study, the longest investigation ever into air
pollution and kids’ health. Between 1993 and 2001, study scientists from the Keck School
tracked levels of major pollutants in 12 Southern California communities while following
the pulmonary health of 1,759 children as they progressed from 4th grade to 12th grade.
The 12 communities included some of the most polluted areas in the greater Los Angeles
basin, as well as several low-pollution sites outside the area.
Keck School researchers previously found that children who were exposed to more air
pollution scored more poorly on respiratory tests. In this latest study, published in the
Sept. 9 issue of the journal, researchers analyzed the same children’s respiratory health at
age 18, when lungs are almost completely mature.
“Teenagers in smoggy communities were nearly five times as likely to have clinically low
lung function, compared to teens living in low-pollution communities,” explained W.
James Gauderman, associate professor of preventive medicine at the Keck School and lead
author of the study.
He said that people with clinically low lung function have less than 80 percent of the lung
function expected for their age�a significant deficit that would raise concerns during a
doctor’s exam.
“When we began the study 10 years ago, we had no idea we would find effects on the lung
this serious,” said John Peters, Hastings Professor of Preventive Medicine at the Keck
School, director of the Southern California Environmental Health Sciences Center, and
senior author of the study.
https://news.usc.edu/category/health/
https://news.usc.edu/author/alicia-di-rado/
Study technicians traveled to participating schools every year and tested children’s lung
function, a measure of how well their lungs work. As an example, someone with sub-par
lung function cannot exhale and blow up a balloon as quickly or as big as someone with
good lung function.
Researchers correlated the students’ lung health measurements with levels of air
pollutants monitored in the communities during the same time period.
They found greater deficits in lung development in teenagers who lived in communities
with higher average levels of nitrogen dioxide, acid vapor, particulate matter with a
diameter of less than 2.5 micrometers (about a tenth the diameter of a human hair) and
elemental carbon.
“These are pollutants that all derive from vehicle emissions and the combustion of fossil
fuels,” said Gauderman.
Deficits in lung function have both short- and long-term effects. “If a child or young adult
with low lung function were t ...
Studies Say Indoor Air Pollution Increases Asthma Symptoms Among Childrenlegacyheatingandairinc
In 2009, researchers at Johns Hopkins University found a significant correlation between rising levels of air pollution in homes and the severity of asthma symptoms among children. Eight years after that research, several studies revealed that poor indoor air quality continues to produce adverse effects on children with asthma.
The $22 Billion Idle Exhaust Cloud.
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More Related Content
Similar to The Impact of an Anti-IDLING Campaign on Outdoor Air Quality
1January 2020, Volume 8, Issue 1, Number 17Maryam Bahr.docxaulasnilda
1
January 2020, Volume 8, Issue 1, Number 17
Maryam Bahreynian1 , Marjan Mansourian2 , Nafiseh Mozaffarian3 , Parinaz Poursafa4 , Mehri Khoshhali3* , Roya Kelishadi3
Review Article:
The Association Between Exposure to Ambient Particulate
Matter and Childhood Obesity: A Systematic Review and
Meta-analysis
Context: Physical environment contamination and in particular, air pollution might cause long-term
adverse effects in child growth and a higher risk of catching non-communicable diseases later in life.
Objective: This study aimed to overview the human studies on the association of exposure to
ambient Particulate Matter (PM) with childhood obesity.
Data Sources: We systematically searched human studies published until March 2018 in PubMed,
Scopus, Ovid, ISI Web of Science, Cochrane library, and Google Scholar databases.
Study Selection: All studies that explored the association between PM exposure and childhood
obesity were assessed in the present study, and finally, 5 studies were used in the meta-analysis.
Data Extraction: Two independent researchers performed the data extraction procedure and
quality assessment of the studies. The papers were qualitatively assessed by STROBE (Strengthening
the Reporting of Observational studies in Epidemiology) statement checklist.
Results: The pooled analysis of PM exposure was significantly associated with increased Body Mass
Index (BMI) (Fisher’s z-distribution=0. 028; 95% CI=0. 017, 0. 038) using the fixed effects model. We
also used a random-effect model because we found a significant high heterogeneity of the included
studies concerning the PM (I2=94. 4%; P<0. 001). PM exposure was associated with increased BMI
(Fisher’s z-distribution=0. 022; 95% CI=-0. 057, 0. 102). However, the overall effect size was not
significant, and heterogeneity of the included studies was similar to the fixed effect model.
Discussion: Our findings on the significant association between PM10 exposure and the increased
BMI (r=0. 034; 95%CI=0. 007, 0. 061) without heterogeneity (I2=16. 6%, P=0. 274) (in the studies with
PM10) suggest that the PM type might account for the heterogeneity among the studies.
Conclusion: The findings indicate that exposure to ambient PM10 might have significant effects on
childhood obesity.
A B S T R A C T
Key Words:
Air pollution, Particulate
matter, Childhood
obesity, Meta-analysis
Article info:
Received: 10 Oct 2018
First Revision: 23 Feb 2019
Accepted: 09 Mar 2019
Published: 01 Jan 2020
1. Department of Nutrition Child Growth, and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Dis-
eases, Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran.
2. Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
3. Department of Pediatrics, Child Growth, and Development Research Center, Research Institute for Primordial Preve ...
1January 2020, Volume 8, Issue 1, Number 17Maryam Bahr.docxjesusamckone
1
January 2020, Volume 8, Issue 1, Number 17
Maryam Bahreynian1 , Marjan Mansourian2 , Nafiseh Mozaffarian3 , Parinaz Poursafa4 , Mehri Khoshhali3* , Roya Kelishadi3
Review Article:
The Association Between Exposure to Ambient Particulate
Matter and Childhood Obesity: A Systematic Review and
Meta-analysis
Context: Physical environment contamination and in particular, air pollution might cause long-term
adverse effects in child growth and a higher risk of catching non-communicable diseases later in life.
Objective: This study aimed to overview the human studies on the association of exposure to
ambient Particulate Matter (PM) with childhood obesity.
Data Sources: We systematically searched human studies published until March 2018 in PubMed,
Scopus, Ovid, ISI Web of Science, Cochrane library, and Google Scholar databases.
Study Selection: All studies that explored the association between PM exposure and childhood
obesity were assessed in the present study, and finally, 5 studies were used in the meta-analysis.
Data Extraction: Two independent researchers performed the data extraction procedure and
quality assessment of the studies. The papers were qualitatively assessed by STROBE (Strengthening
the Reporting of Observational studies in Epidemiology) statement checklist.
Results: The pooled analysis of PM exposure was significantly associated with increased Body Mass
Index (BMI) (Fisher’s z-distribution=0. 028; 95% CI=0. 017, 0. 038) using the fixed effects model. We
also used a random-effect model because we found a significant high heterogeneity of the included
studies concerning the PM (I2=94. 4%; P<0. 001). PM exposure was associated with increased BMI
(Fisher’s z-distribution=0. 022; 95% CI=-0. 057, 0. 102). However, the overall effect size was not
significant, and heterogeneity of the included studies was similar to the fixed effect model.
Discussion: Our findings on the significant association between PM10 exposure and the increased
BMI (r=0. 034; 95%CI=0. 007, 0. 061) without heterogeneity (I2=16. 6%, P=0. 274) (in the studies with
PM10) suggest that the PM type might account for the heterogeneity among the studies.
Conclusion: The findings indicate that exposure to ambient PM10 might have significant effects on
childhood obesity.
A B S T R A C T
Key Words:
Air pollution, Particulate
matter, Childhood
obesity, Meta-analysis
Article info:
Received: 10 Oct 2018
First Revision: 23 Feb 2019
Accepted: 09 Mar 2019
Published: 01 Jan 2020
1. Department of Nutrition Child Growth, and Development Research Center, Research Institute for Primordial Prevention of Non-communicable Dis-
eases, Student Research Committee, Isfahan University of Medical Sciences, Isfahan, Iran.
2. Department of Biostatistics and Epidemiology, School of Health, Isfahan University of Medical Sciences, Isfahan, Iran.
3. Department of Pediatrics, Child Growth, and Development Research Center, Research Institute for Primordial Preve.
Designing a Low-Cost Pollution Prevention Plan to Pay Off at.docxcuddietheresa
Designing a Low-Cost Pollution Prevention Plan to Pay Off at
the University of Houston
Yurika Diaz Bialowas, Emmett C. Sullivan, and Robert D. Schneller
Environmental Health and Risk Management Department, University of Houston, Houston, TX
ABSTRACT
The University of Houston is located just south of down-
town Houston, TX. Many different chemical substances
are used in scientific research and teaching activities
throughout the campus. These activities generate a signif-
icant amount of waste materials that must be discarded as
regulated hazardous waste per U.S. Environmental Protec-
tion Agency (EPA) rules. The Texas Commission on Envi-
ronmental Quality (TCEQ) is the state regulatory agency
that has enforcement authority for EPA hazardous waste
rules in Texas. Currently, the University is classified as a
large quantity generator and generates �1000 kg per
month of hazardous waste. In addition, the University
has experienced a major surge in research activities during
the past several years, and overall the quantity of the
hazardous waste generated has increased. The TCEQ re-
quires large quantity generators to prepare a 5-yr Pollu-
tion Prevention (P2) Plan, which describes efforts to elim-
inate or minimize the amount of hazardous waste
generated. This paper addresses the design and develop-
ment of a low-cost P2 plan with minimal implementation
obstacles and strong payoff potentials for the University.
The projects identified can be implemented with existing
University staff resources. This benefits the University by
enhancing its environmental compliance efforts, and the
disposal cost savings can be used for other purposes.
Other educational institutions may benefit by undertak-
ing a similar process.
INTRODUCTION
The University of Houston campus covers �550 acres and
includes �100 buildings. Current enrollment is �35,000
students with �5000 faculty and staff.1 There are �400
laboratories on campus presently with more planned in
2006.
Colleges and universities typically generate a wide
range of chemical waste and, because of their decentral-
ized organizational structure face, challenges in comply-
ing with applicable waste regulations.2,3 One of the criti-
cal functions of the University’s Environmental Health
and Risk Management Department (EHRM) is to manage
chemical waste in accordance with the U.S. Environmen-
tal Protection Agency (EPA) and Texas Commission on
Environmental Quality (TCEQ) rules.4,5 This is a sizable
undertaking for the EHRM staff and ties up many re-
sources. There is no formal long-term investment in waste
disposal costs for the University other than a demonstra-
tion of regulatory compliance.6 Therefore, it is in the best
interest of the University to minimize the quantity of
chemical waste generated whenever possible so that funds
could be spent on projects other than waste disposal.
In addition, the TCEQ has promulgated rules that
require large-quantity hazardous waste generators, such
as the University, t ...
Identifying and Prioritizing Chemicals with Uncertain Burden oMalikPinckney86
Identifying and Prioritizing Chemicals with Uncertain Burden of Exposure:
Opportunities for Biomonitoring and Health-Related Research
Edo D. Pellizzari,1 Tracey J. Woodruff,2 Rebecca R. Boyles,3 Kurunthachalam Kannan,4 Paloma I. Beamer,5 Jessie P. Buckley,6
Aolin Wang,2 Yeyi Zhu,7,8 and Deborah H. Bennett9 (Environmental influences on Child Health Outcomes)
1Fellow Program, RTI International, Research Triangle Park, North Carolina, USA
2Program on Reproductive Health and the Environment, Department of Obstetrics, Gynecology and Reproductive Sciences, University of California, San
Francisco, San Francisco, California, USA
3Bioinformatics and Data Science, RTI International, Research Triangle Park, North Carolina, USA
4Wadsworth Center, New York State Department of Health, Albany, New York, USA
5Department of Community, Environment and Policy, Zuckerman College of Public Health, University of Arizona, Tucson, Arizona, USA
6Department of Environmental Health and Engineering, Johns Hopkins Bloomberg School of Public Heath, Johns Hopkins University,
Baltimore, Maryland, USA
7Northern California Division of Research, Kaiser Permanente, Oakland, California, USA
8Department of Epidemiology and Biostatistics, University of California, San Francisco, San Francisco, California, USA
9Department of Public Health Sciences, University of California, Davis, Davis, California, USA
BACKGROUND: The National Institutes of Health’s Environmental influences on Child Health Outcomes (ECHO) initiative aims to understand the
impact of environmental factors on childhood disease. Over 40,000 chemicals are approved for commercial use. The challenge is to prioritize chemi-
cals for biomonitoring that may present health risk concerns.
OBJECTIVES: Our aim was to prioritize chemicals that may elicit child health effects of interest to ECHO but that have not been biomonitored nation-
wide and to identify gaps needing additional research.
METHODS: We searched databases and the literature for chemicals in environmental media and in consumer products that were potentially toxic. We
selected chemicals that were not measured in the National Health and Nutrition Examination Survey. From over 700 chemicals, we chose 155 chemi-
cals and created eight chemical panels. For each chemical, we compiled biomonitoring and toxicity data, U.S. Environmental Protection Agency ex-
posure predictions, and annual production usage. We also applied predictive modeling to estimate toxicity. Using these data, we recommended
chemicals either for biomonitoring, to be deferred pending additional data, or as low priority for biomonitoring.
RESULTS: For the 155 chemicals, 97 were measured in food or water, 67 in air or house dust, and 52 in biospecimens. We found in vivo endocrine, de-
velopmental, reproductive, and neurotoxic effects for 61, 74, 47, and 32 chemicals, respectively. Eighty-six had data from high-throughput in vitro
assays. Positive results for endocrine, developmental, neurotoxicity, ...
Why is it so hard to reduce household air pollution among the very poor?Leith Greenslade
What cooking technologies can deliver lasting reductions in exposure to household air pollution among the very poor? This is THE question. Learn more from four experts, including Neil Schluger and Darby Jack (Columbia University), Alison Lee (Icahn School of Medicine Mt Sinai) and Joshua Rosenthal (NIH), on the latest research and the most promising technologies, especially the new efforts to reroute government fuel subsidies from the middle class to the very poor (e.g. India Give it Up Campaign for LPG).
Dr. Fred C. Lunenburg[1]. environmental hazards in america's schools focus v4...William Kritsonis
Dr. Fred C. Lunenburg, www.nationalforum.com, Dr. William Allan Kritsonis, Editor-in-Chief, National FORUM Journals, Houston, Texas
www.nationalforum.com
By age 18, the lungs of many children who grow up in smoggy ar.docxRAHUL126667
By age 18, the lungs of many children who grow up in smoggy areas are underdeveloped and will likely
never recover, according to a study by Keck School of Medicine researchers in the New England Journal of
Medicine.
Health [/category/health/]
USC study links smoggy air to
lung damage in children
BY Alicia Di Rado [/author/alicia-di-rado/] SEPTEMBER 17, 2004
The research is part of the Children’s Health Study, the longest investigation ever into air
pollution and kids’ health. Between 1993 and 2001, study scientists from the Keck School
tracked levels of major pollutants in 12 Southern California communities while following
the pulmonary health of 1,759 children as they progressed from 4th grade to 12th grade.
The 12 communities included some of the most polluted areas in the greater Los Angeles
basin, as well as several low-pollution sites outside the area.
Keck School researchers previously found that children who were exposed to more air
pollution scored more poorly on respiratory tests. In this latest study, published in the
Sept. 9 issue of the journal, researchers analyzed the same children’s respiratory health at
age 18, when lungs are almost completely mature.
“Teenagers in smoggy communities were nearly five times as likely to have clinically low
lung function, compared to teens living in low-pollution communities,” explained W.
James Gauderman, associate professor of preventive medicine at the Keck School and lead
author of the study.
He said that people with clinically low lung function have less than 80 percent of the lung
function expected for their age�a significant deficit that would raise concerns during a
doctor’s exam.
“When we began the study 10 years ago, we had no idea we would find effects on the lung
this serious,” said John Peters, Hastings Professor of Preventive Medicine at the Keck
School, director of the Southern California Environmental Health Sciences Center, and
senior author of the study.
https://news.usc.edu/category/health/
https://news.usc.edu/author/alicia-di-rado/
Study technicians traveled to participating schools every year and tested children’s lung
function, a measure of how well their lungs work. As an example, someone with sub-par
lung function cannot exhale and blow up a balloon as quickly or as big as someone with
good lung function.
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The Impact of an Anti-IDLING Campaign on Outdoor Air Quality
1. The impact of an anti-idling campaign on outdoor air
quality at four urban schools
Patrick H. Ryan,*ab
Tiina Reponen,b
Mark Simmons,b
Michael Yermakov,b
Ken Sharkey,c
Denisha Garland-Porter,c
Cynthia Eghbalniad
and Sergey A. Grinshpunb
Idling school buses may increase concentrations of air pollutants including fine particulate matter (PM2.5)
and elemental carbon (EC) near schools. Efforts to reduce vehicle idling near schools have rarely included
air sampling to objectively assess changes in concentrations of air pollutants. The objective was to
determine the impact of an anti-idling campaign on outdoor air quality at four schools with varying
exposure to bus and automobile traffic. Outdoor air sampling for PM2.5, EC and particle number
concentration (PNC) was conducted at four schools for five days before and after an anti-idling
campaign. Sampling began before the morning arrival of buses and concluded after their afternoon
departure. Sampling was simultaneously conducted at four corresponding community sites. Differences
in PM2.5, EC, and PNC measured at school and community sites for each sampling day were calculated
before and after the campaign. Before the campaign, the average outdoor concentration of PM2.5
during the school day at three of the four schools exceeded community background levels and the
difference was greatest (4.11 mg mÀ3
, p < 0.01) at the school with the most buses (n ¼ 39). The largest
difference in EC between school and community sites was also observed at the school with the greatest
number of buses (0.40 mg mÀ3
, p < 0.01). Following the anti-idling campaign, the average difference in
PM2.5 at the school with the most buses decreased from 4.11 mg mÀ3
to 0.99 mg mÀ3
(p < 0.05).
Similarly, at this school, the difference in the EC level decreased from 0.40 mg mÀ3
to 0.15 mg mÀ3
and
PNC decreased from 11 560 to 1690 particles per cm3
(p < 0.05). The outdoor concentrations of
pollutants at schools with fewer buses (n ¼ 5–11) were not significantly reduced. The concentration of
air pollutants near schools may significantly exceed community background levels, particularly in the
presence of idling school buses. Anti-idling campaigns are effective in reducing PM2.5, EC and PNC at
schools with significant amounts of buses and passenger cars.
Environmental impact
Children spend a signicant amount of their time at schools where exposure to traffic-related air pollutants may be elevated due to idling buses. Though efforts
have been made to reduce idling near schools, there have been no studies, to our knowledge, which have quantitatively measured changes in air quality before
and aer an anti-idling campaign. In this paper, we report that traffic-related air pollutants are frequently elevated in the vicinity of schools compared to
residential neighborhoods. In addition, we demonstrate that an anti-idling campaign, a simple public health intervention, can signicantly reduce air
pollutants at schools with potential benets to the health of children who attend these schools.
1. Introduction
The areas immediately surrounding major roadways are
frequently referred to as ‘traffic hot spots’ – locations where
levels of traffic-related air pollution (TRAP) are elevated.
Residing in close proximity to these traffic sources has been
associated with both exacerbation and development of
asthma.1–3
The microenvironment of children, however, includes
locations outside of the home, including schools and inside
vehicles during transit to and from school. The school environ-
ment may be especially relevant to TRAP exposure as a nationwide
survey found that 20–44% of public schools in nine metropolitan
regions were located within 400 m of an interstate or highway
where levels of TRAP have been shown to be elevated.4
Few
investigations have addressed the health impact of school expo-
sures, though a longitudinal cohort study in southern California
has reported both home and school exposure to be associated
with the development of new asthma in school-age children.1
a
Cincinnati Children's Hospital Medical Center, Division of Biostatistics and
Epidemiology, Cincinnati Children's Hospital Medical Center, 3333 Burnet Ave, ML
5041, Cincinnati, OH 45229, USA. E-mail: patrick.ryan@cchmc.org; Fax: +1 513-
636-7509; Tel: +1 513-803-4704
b
University of Cincinnati, Department of Environmental Health, Cincinnati, OH, USA
c
Cincinnati Health Department, Cincinnati, OH, USA
d
Cincinnati Public Schools, Cincinnati, OH, USA
Cite this: Environ. Sci.: Processes
Impacts, 2013, 15, 2030
Received 16th July 2013
Accepted 3rd September 2013
DOI: 10.1039/c3em00377a
rsc.li/process-impacts
2030 | Environ. Sci.: Processes Impacts, 2013, 15, 2030–2037 This journal is ª The Royal Society of Chemistry 2013
Environmental Science
Processes & Impacts
PAPER
2. Exposure to TRAP near schools may also be elevated due to
the impact of local traffic and, in particular, diesel-fueled
schools buses. Diesel exhaust particles (DEP), the majority of
which are ultrane in size (aerodynamic diameter <0.1 mm), are
comprised of an elemental carbon core with a large surface area
to which chemicals including polycyclic aromatic hydrocarbons
(PAHs) and transition metals may be attached.5
The presence of
school buses has been associated with signicantly increased
particle number concentration (particularly in the ultrane
fraction) in the near-school ambient air.6,7
Studies have also
reported that school bus idling can be a signicant predictor of
black carbon and PM2.5 concentrations in the school vicinity.8,9
To improve air quality near schools, many communities
support anti-idling efforts, retrotting of school buses with
diesel oxidation catalysts, and the implementation of alterna-
tive fuels including low-sulfur diesel fuel. However, to our
knowledge, anti-idling efforts have not been objectively evalu-
ated with accompanying measurements of health-relevant air
pollutants. The Cincinnati Anti-Idling Campaign (CAIC), a
partnership between academic researchers and community
members from local schools and health departments, was
designed to determine whether children are exposed to
increased levels of TRAP, including ultrane particles and
diesel-related elements, while at school and to develop and
implement a community-driven anti-idling campaign to reduce
exposure to TRAP at schools. The objectives of this analysis were
to: (1) compare ambient air monitoring data collected outside of
four urban schools to the background levels measured at the
corresponding community sites and (2) compare results of pre-
and post-anti-idling measurements performed at four schools
participating in the CAIC study in order to quantitatively
demonstrate the effectiveness of an anti-idling campaign.
2. Methods
2.1. Study sites
The study included four public schools, henceforth referred to
as Schools A, B, C, and D, selected to participate based upon the
prevalence of asthma reported by parents and potential expo-
sure to TRAP emitted from nearby major roads and by school
buses. Participating schools were selected from grade schools
(pre-Kindergarten through grade 8) whose prevalence of parent
reported asthma exceeded 10%. Of these schools, one was
chosen to participate from each of the following a priori dened
categories: (1) major road <400 m from school, low bus traffic
(School A), (2) major road >400 m from school, high bus traffic
(School B), (3) major road <400 m from school, medium bus
traffic (School C), and (4) major road >400 m, low bus traffic
(School D). For each of the selected school, an outdoor air
monitoring site was established. School sampling stations were
generally located at a school entrance nearest to the bus drop
off/pick up areas. In addition, the geographic area where chil-
dren attending each school reside was identied and an
outdoor community air monitoring site was established within
this catchment area. Site selection criteria for community sites
included being greater than 400 m from the nearest major road,
having access during the school day and electrical power.
Neighborhood community centers served as community sites
for Schools A, B, and D. A private residence was used as the
community site for school C.
2.2. School and community air monitoring
Pre- and post-anti-idling campaign air monitoring was con-
ducted for each school and their corresponding community
sites for ve school days in the spring of 2010 and ve school
days in the spring of 2011. For each school, air sampling at the
school and community sites was conducted concurrently.
Sampling was scheduled for days with forecasted precipitation
<6 mm and no unusual activities at the sites. A complete
description of the sampling methods is available elsewhere.6
Briey, sampling at both the community and school sites began
approximately 30 minutes prior to the rst school bus arriving
in the morning and concluded approximately 30 minutes aer
the last school bus le in the aernoon. Average pollutant
concentrations during this approximately nine-hour sampling
period (7 AM to 4 PM) were derived. At each sampling site, two
Harvard-type PM2.5 impactors (Air Diagnostics and Engineering,
Inc. Harrison, ME) were operated in parallel. The Harvard
impactor has a cut size of 2.5 mm at a sampling ow rate of 20 L
minÀ1
and particles were collected on two 37 mm lters: one
Teon (Pall Corp., Ann Arbor, MI) for PM2.5 mass measurement
and elemental analysis by X-ray uorescence and one quartz lter
(Whatman, Inc., Clion, NJ) for elemental carbon analysis by
thermal-optical transmittance. Two blank samples were collected
and analyzed for each sampling location. All analyses were per-
formed by Chester LabNet Inc. (Tigard, OR).
A portable condensation nuclei counter (P-Trak, Model 8525,
TSI Inc., St. Paul, MN) was also operated at each sampling
location to monitor the particle number concentration of
aerosol particles during the sampling period. The P-Trak is
capable of non-size discriminating real-time counting of parti-
cles from 20 nm to >1 mm. The particle number concentration
was recorded as a time-series with a resolution of 1 minute. For
this analysis, the average particle number concentration (PNC)
during the peak morning drop-off period of each school was
calculated as the average of the 1 minute particle concentration
data from 30 minutes prior to the start of the school day until
the start of the school day by which time all children had
entered the school. Data obtained from the community-site P-
trak instrument were extracted for the identical time period of
each corresponding school and the average PNC concentration
was calculated. Results of P-Trak measurements are expressed
as particles per cm3
.
2.3. Cincinnati anti-idling campaign
The objectives, methods, and results of the Cincinnati Anti-
Idling Campaign (CAIC) have been described in detail else-
where.10
Briey, the CAIC consisted of four components
including research and development, campaign activities,
online training videos, and implementation of the EPA Tools for
Schools. Key contacts including teachers, administrators, and
parents at each participating school were enlisted to assist with
the campaign. A school-bus driver educational program was
This journal is ª The Royal Society of Chemistry 2013 Environ. Sci.: Processes Impacts, 2013, 15, 2030–2037 | 2031
Paper Environmental Science: Processes & Impacts
3. presented to all bus drivers followed by an anti-idling pledge
drive. Information was also provided to parents accompanied with
a pledge to reduce idling. Other CAIC activities included school
bus monitoring, all school air quality assemblies, and anti-idling
signs placed near the school drop-off/pick-up zones. CAIC activi-
ties were conducted during the fall and winter 2010–2011.
Following the anti-idling campaign, nearly 400 bus drivers volun-
tarily pledged to stop idling which was conrmed by a signicant
reduction in observed vehicle idling time post-campaign.10
2.4. Statistical analysis
The objective of the statistical analyses was to compare
concentrations of PM2.5, EC, and particle number concentra-
tion (PNC) at schools to their respective community sites and
also to assess changes in these pollutant concentrations aer
the anti-idling campaign. Sampling data collected in the spring
2010 were considered pre-anti-idling campaign (baseline) and
data collected in the spring of 2011 were considered post-anti-
idling campaign data. Summary statistics of the concentration
levels for the air quality measurements were obtained for each
school–year combination. In order to characterize air quality for
each school and year accounting for community/background
concentrations, the concentration of each pollutant at the
community location was subtracted from the concentration at
the school site for each day of sampling (i.e., values >0 indicate
that the average concentration at the school site exceeds back-
ground levels). Thus, the difference in concentration levels
between the school sites and the community/background
location for each site was the primary outcome variable in the
statistical analyses. A general linear model was developed for
each of the three air quality measurements (PM2.5, EC, PNC) as
a function of the two xed effects representing the school site
and year, along with an interaction effect:
(concentration difference of X) $ b0 + b1ischool sitei + b2jyearj +
b3ij(school site) Â (year) + 3ij
where X ¼ the air quality measure (PM2.5, EC, PNC), b0
s ¼
regression coefficients, i ¼ school site (i ¼ 1,.,4), j ¼ year (j ¼
1,2), and 3 ¼ the residuals.
Concentrations of the selected air pollutants were not
available perfectly for all days of sampling at all sites due to
equipment failure creating an unbalanced study design.
Specically, one day of PNC data for School A was not available
in 2010. For School C, one day of EC data and two days of PNC
data for both 2010 and 2011 were missing. For School D, one
day of PNC in 2010 as well as two days of PNC and EC were
missing in 2011. There were no missing data points for School
B. The statistical analysis was performed using SAS Version 9.2.
PROC MIXED was used to model the unequal variances among
the different school–year combinations. The model assump-
tions of normality, heterogeneity, and linearity were assessed
for the tted model. The signicance of each main effect and
the interaction effect was determined using an F-test statistic
and the overall goodness-of-t for the model was assessed by
the log-likelihood ratio statistic and the Akaike Information
Criterion (AIC) statistic. Since the study design was unbalanced,
the least-squares (LS) means were computed for each school–
year combination and a t-test was performed to test the null
hypothesis that each specic school–year LS-mean equals zero.
This hypothesis test of the concentration difference was equiva-
lent to testing whether or not the concentration levels at the school
bus drop-off location were different from the community levels.
A test of simple effects was performed using an F-test statistic to
test the effect of year for each level of school, and vice versa.
3. Results
The characteristics of the participating schools are presented in
Table 1. Schools A and C were located less than 400 m from a
major road. However, School A was near a federal interstate
highway with approximately 7 times the number of daily
passenger vehicle traffic (130 198 vs. 17 080) and nearly 60 times
the number of daily trucks (17 305 vs. 290) than School C. The
number of buses at each school ranged from 5 (School A) to 39
(School B). School B also had the highest average number of
cars during drop-off (n ¼ 77) during the weeks of air sampling.
3.1. PM2.5 results
The average PM2.5 concentrations at school and community sites
before and aer the anti-idling campaign are presented in Fig. 1.
The concentration of PM2.5 at community sites ranged from 12.2
to 17.6 mg mÀ3
and PM2.5 concentrations at schools sites were
similar (13.1–17.9 mg mÀ3
) (Fig. 1A). The highest overall average
PM2.5 concentration was observed at School B, which was not
located near major roads but had, on average, 39 buses per day.
Following the anti-idling campaign, the concentration of PM2.5
at school sites ranged from 9.0 to 20.5 mg mÀ3
while background
concentrations ranged from 9.8 to 21.0 mg mÀ3
(Fig. 1B).
The average differences of PM2.5 concentrations between
school and community sites before and aer the anti-idling
campaign are presented in Table 2. Prior to the campaign, the
concentrations of PM2.5 at schools exceeded those at the
background site at three of the four schools, and was signi-
cantly greater than the background concentration at School B
(average difference D ¼ 4.11 mg mÀ3
, p < 0.01). Following the
anti-idling campaign, the average level of PM2.5 at School B was
the only location exceeding the background site (D ¼ 0.99 mg
mÀ3
, p < 0.01). The change in average school-background
differences due to the anti-idling campaign was signicant for
Schools B (4.11 to 0.99 mg mÀ3
) and D (0.48 to À1.35 mg mÀ3
). In
the case of School D, average community concentrations of
PM2.5 exceeded school concentrations aer the anti-idling
campaign. This signicant change may be a result of reduced
idling at the school resulting in community levels exceeding
school concentrations or changes in background concentra-
tions of PM2.5 in the community.
3.2. EC results
Fig. 2 presents the average concentrations of EC before and aer
the anti-idling campaign at school and community sites. The
concentration of EC at the four schools prior to the anti-idling
campaign ranged from 0.06 at School C to 0.77 mg mÀ3
at School
2032 | Environ. Sci.: Processes Impacts, 2013, 15, 2030–2037 This journal is ª The Royal Society of Chemistry 2013
Environmental Science: Processes & Impacts Paper
4. B. Corresponding community site levels ranged from 0.05 to
0.62 mg mÀ3
and were lower than those at school sites with the
exception of School A (Fig. 2). Following the anti-idling
campaign, the concentration of EC at both school and
community sites increased for Schools A and C while decreased
EC concentrations were observed at Schools B and D.
Prior to the anti-idling campaign, concentrations of EC at
School B were signicantly greater than the corresponding
community levels (D ¼ 0.40 mg mÀ3
, p < 0.05). There were no
other signicant differences observed for EC between school
and community sites prior to the campaign. Following the
campaign, the concentration of EC at School B was no longer
signicantly elevated compared to background levels (Table 2).
In addition, the change in average difference between school
and community levels at school B was signicantly reduced
(0.40 to 0.15 mg mÀ3
, p ¼ 0.05). However, at School A, the average
difference between school and community levels increased
(À0.18 to 0.22 mg mÀ3
, p ¼ 0.01).
3.3. Particle number concentration results
Both before and aer the anti-idling campaign, the average PNC
at community sites exceeded that at school sites with the
exception of the school with the greatest number of buses
(School B, Fig. 3). Prior to the anti-idling campaign, the lowest
PNCs at both school and community sites were observed at
Table 1 Characteristics of participating schoolsa
Characteristic
School
A B C D
Year built 2007 2007 1962 2005
Distance to the nearest
major road* (m)
303 526 243 2083
Average daily passenger
vehicle count on the
nearest major road
130 198 8310 17 080 136 530
Average daily truck count
on the nearest major road
17 305 210 290 19 710
Average number of buses
per arrival/departure
5 39 11 9
Average number of cars/
drop-off
18 77 27 24
Prevalence of parental
reported asthma
10% 10% 15% 12%
Description of school/
community
Urban school/community
located near a major
interstate highway and
other industrial and
transportation sources
Urban school/
community with no
nearby transportation
or industrial sources
Urban/suburban school/
community with moderate
nearby traffic sources
Urban school community
with nearby major
industrial sources
a
Major road dened as U.S. interstate, U.S. highway, or state highway.
Fig. 1 Pre- and post-anti-idling campaign average PM2.5 (+1 SE) at school and community sites.
This journal is ª The Royal Society of Chemistry 2013 Environ. Sci.: Processes Impacts, 2013, 15, 2030–2037 | 2033
Paper Environmental Science: Processes & Impacts
5. School C (10 480 and 10 920 particles per cm3
) while the largest
observed PNCs occurred at School A sites (Fig. 3). As seen in
Fig. 3, PNC sampled aer the anti-idling campaign was
generally lower than before the campaign at both community
and school sites. The differences in average PNC between
school and community sites before and aer the anti-idling
Table 2 Average difference (D)* in PM2.5, elemental carbon (EC), and particle number concentration (PNC) between school and community sampling sitesa
School
A B C D
Pre-
anti-idling
Post-
anti-idling p
Pre-
anti-idling
Post-
anti-idling p
Pre-
anti-idling
Post-
anti-idling p
Pre-
anti-idling
Post-
anti-idling p
DPM2.5
(mg mÀ3
)
À0.95 À0.52 0.77 4.11** 0.99** 0.04 0.9 À4.71 0.33 0.48 À1.35 0.03
DEC (mg mÀ3
) À0.18 0.22** 0.01 0.40** 0.15 0.05 À0.01 À0.04 0.55 0.16 0.01 0.27
DPNC
(particles per c3
)
À15 500** À3630 0.02 11 560** 1690 0.01 À440 920 0.59 À7250** À4130** 0.01
a
*Community site concentration subtracted from school concentration. D > 0 indicates that the average concentrations at the school site exceeded
community levels. D < 0 indicates that community levels exceeded those at school sites. **Difference in school and community concentrations (p <
0.05) signicantly differs from 0.
Fig. 2 Pre- and post-anti-idling campaign average elemental carbon concentrations (+1 SE) at school and community sites.
Fig. 3 Pre- and post-anti-idling campaign average particle number concentration (+1 SE) at school and community sites.
2034 | Environ. Sci.: Processes Impacts, 2013, 15, 2030–2037 This journal is ª The Royal Society of Chemistry 2013
Environmental Science: Processes & Impacts Paper
6. campaign are presented in Table 2. Before the anti-idling
campaign, the average PNC at school sites was signicantly less
than that at community sites for Schools A and C (D ¼ À15 500
and D ¼ À7.250 particles per cm3
) while at one location (School
B) the PNC at the school site signicantly exceeded background
PNC levels (D ¼ 11560 particles per cm3
, p < 0.01) (Table 2).
Following the anti-idling campaign, only School D continued to
have a signicant difference in PNC between community and
school sites (D ¼ À4130 p cmÀ3
). In addition, the difference in
school and community PNC differences was signicantly
reduced at Schools A, B, and D (Table 2).
4. Discussion
To our knowledge, this is the rst study to assess the effec-
tiveness of an anti-idling campaign by pre- and post-campaign
air monitoring for PM2.5, EC, and PNC. The results of this study
support the assumption that anti-idling efforts are successful in
reducing traffic-related particle exposure. In particular, we
observed signicant reductions in PM2.5, EC, and PNC at
School B, which had the largest number of school buses (n ¼ 39)
in the school vicinity. Changes in idling behavior at schools
with fewer buses (5–11) were not reected by changes in
measured air pollutants.
This research effort was undertaken as part of an academic-
community partnership with the objectives of determining
whether children are exposed to increased levels of TRAP at
schools compared to their communities, to develop and
implement a community-driven anti-idling campaign, and to
evaluate the effectiveness of the intervention to reduce TRAP at
schools with the goal of improving the health of children who
attend the schools10
The concurrent sampling of community
and school outdoor concentrations of PM2.5, EC, and PNC
allowed examining the changes in air quality at the schools
relative to changes in community air quality. In a study of PM2.5
and black carbon (BC, surrogate for EC) near four schools in
New York City, variability in background concentrations was
assessed using sites throughout the city and found to contribute
approximately 80% of the variability in air pollutants at the
schools.8
This study also found local traffic and diesel idling to
be signicant contributors to outdoor concentrations of these
pollutants measured at school.8
In our study, we attempted to
identify community sites within the catchment area for each
school in order to obtain representative ambient air measure-
ments for the neighborhoods in which children resided. Using
this approach, we found that in 3 of the 4 schools, community
levels of PM2.5 were not signicantly different from those at
school sites. These ndings are not unexpected given the large
spatial variation of PM2.5 (ref. 11) which exceeds the distance
from school to community sites. Nevertheless, our nding of
PM2.5 concentrations at School B exceeding community levels
suggests that local bus and car traffic at the school (which was
not located near major roads) are signicant contributors to
PM2.5 at that site.
Despite efforts to reduce idling near schools, there have been
few studies which measured the inuence of idling school
buses on air quality. Idling diesel school buses and trucks
produce particles primarily in the ultrane size range and these
have been associated with respiratory and neurologic health
effects due to their deposition in the lower respiratory tract and
potential to impact the brain.12,13
The small size and large
surface area of diesel exhaust particles allow for numerous
compounds to be attached to the elemental carbon core
including low-molecular weight hydrocarbons such as alde-
hydes, benzene, polycyclic aromatic hydrocarbons (PAHs) and
nitro-PAHs. Diesel exhaust is also comprised of sulfate, nitrate,
metals, and other trace elements which have been linked to
health outcomes.14
In the US, approximately 24 million students
are transported to school on nearly 600 000 school buses, the
majority of which are diesel fueled.15
Compared to emissions
following engine restart, idling school buses have been found to
contribute to signicantly increased concentrations of ne PM,
BC, EC, and ultrane particle number concentration.4,7,8,16–18
Short term exposure to DEP and UFP, as may occur during
school bus idling, has been shown to elicit acute decreases in
lung function and increases in neutrophilic inammation
among asthmatics in a roadside environmental experiment.19
A frequent limitation of many epidemiologic studies of air
pollution and health outcomes is the use of only home
addresses to characterize exposure. Children, in particular,
spend most of their time outside their home and exposures at
schools or in transportation to school are likely to account for a
signicant proportion of a child's overall exposure to TRAP.20
Despite this, there have been few studies which have examined
or accounted for school exposures and associated health
outcomes. In a French cross-sectional study, indoor air pollut-
ants, including formaldehyde and PM2.5, were found to be
signicantly associated with rhinoconjunctivitis and asthma
symptoms in the previous year.21
In the Southern California
Children's Health Study, the risk for developing new onset
asthma was found to be associated with exposure to TRAP at
both homes and schools.1
Components of the TRAP mixture, including EC, ultrane
particles (particles <100 nm in diameter), NOx, and other
pollutants, exhibit high spatial variability with elevated
concentrations within approximately 400 m or less from major
roads.11,22,23
It has been recognized that schools located in the
vicinity of nearby roads may have elevated levels of these
pollutants outside and studies in both the U.S. and Canada have
found a signicant number of schools located near major
roads.4,24
The impact of nearby roads on both schools and
communities is evident for School A, which is located near a
major interstate highway, and had the highest levels of PM2.5
and EC during the post-anti-idling campaign sampling period.
There are, however, several limitations to this study. Schools
selected to participate in the anti-idling campaign were chosen
based upon the prevalence of asthma among students, the
number of school buses, and nearby sources of TRAP. Cincin-
nati Public Schools, however, are primarily neighborhood
schools with many students living in close proximity. Thus, the
average number of buses serving K-8 schools in CPS is 9 and in
our study the number of buses ranged from 5–39. The impact of
the anti-idling campaign on air quality was particularly evident
at School B which had 39 buses. Therefore, we speculate that
This journal is ª The Royal Society of Chemistry 2013 Environ. Sci.: Processes Impacts, 2013, 15, 2030–2037 | 2035
Paper Environmental Science: Processes & Impacts
7. anti-idling campaigns are likely to have the greatest impact on
air pollutant concentrations in school districts with a greater
number of buses. Another limitation to this study is the number
of available air measurements (5 pre- campaign, 5 post-
campaign) which does not provide information on long-term
trends of air quality near the participating schools. Meteoro-
logical conditions are also important factors which inuence
the dispersion of emissions and their impact. In particular,
wind direction may play an important role in the impact of
nearby traffic sources on sampled pollutant concentrations.
However, our primary outcome (differences in pollutant
concentrations between school and community sites) is less
likely to be impacted by meteorological conditions as these are
not expected to signicantly vary from each school to its cor-
responding community site on each day of sampling. Never-
theless, we acknowledge that local, short-term wind patterns
may play a role in the sampled concentrations. A particular
strength of our analysis is the use of a mixed linear model which
allows for the difference in daily school-community site
concentrations to be examined rather than average concentra-
tions for the sampling period (over which meteorological
conditions are more likely to vary). This approach also increases
the power of the analysis to detect signicant differences.
In conclusion, we have shown that the outdoor concentra-
tions of PM2.5, EC, and PNC are signicantly elevated
compared to background levels at a school with a large number
of school buses. In addition we have quantitatively demon-
strated a reduction in PM2.5, EC, and PNC aer an anti-idling
campaign. Despite a nationwide anti-idling campaign initiated
by the U.S. EPA (National Idle-Reduction Campaign, NIRC) as a
part of its Clean School Bus USA program, the School Health
Policies and Programs Study (SHPPS) has recently reported that
56.6% of surveyed school districts had not implemented any
school bus idling reduction program.25
Implementation and
enforcement of anti-idling statutes near schools should be
considered as part of a multi-faceted approach to improve air
quality at schools where children spend large amounts of their
time and may be exposed to freshly emitted diesel exhaust when
entering, exiting, or playing near idling school buses.
Acknowledgements
The researchers would like to thank the participating Cincin-
nati Public Schools. This research was funded by the National
Institute of Environmental Health Sciences, grant number
R21ES017957.
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