LUNG CANCER, CARDIOPULMONARY MORTALITY, AND LONG-TERM EXPOSURE TO FINE PARTICULATE AIR POLLUTION have been found between day-to-day particulate air pollution and increased risk of various adverse health outcomes, including cardiopulmonary mortality. However, studies of health effects of long-term particulate air pollution have been less conclusive. This study is to assess the relationship between long-term exposure to fine particulate air pollution and all-cause, lung cancer, and cardiopulmonary mortality. The research methods, vital status and cause of death data were collected by the American cancer society as part of the Cancer prevention II study, an ongoing prospective mortality study, which enrolled approximately 1.2 million adults in 1982. Participants completed questionnaire detailing individual risk factor data (age, sex, race, weight, height, smoking history, education, marital status, diet, alcohol consumption, and occupational exposure). The risk factor data approximately 500, 000 adults were linked with air pollution data for metropolitan areas throughout the United States and combined with vital status and cause of death data through December 31, 1998. Main outcome measure, all-cause, lung cancer, and cardiopulmonary mortality. Result of the study, fine particulate and sulfur oxide-related pollution were associated with all-cause, lung cancer, and cardiopulmonary mortality. Each 10nanogram/meter cube elevation in fine particulate air pollution was associated with approximately a 4%, 6%, and 8% increased risk of all-cause, cardiopulmonary, and lung cancer mortality, respectively. Measures of coarse particle fraction and total suspended particles were not consistently associated with mortality. Therefore, long-term exposure to combustion- related fine particulate air pollution is an important environment risk factor for cardiopulmonary and lung cancer mortality.
THE RELATION BETWEEN PAST EXPOSURE TO FINE PARTICULATE AIR POLLUTION AND PREVALENT ANXIETY: OBSERVATIONAL COHORT, to determine whether higher past exposure to particulate air pollution is associated with prevalent high symptoms of anxiety. Design Observational cohort study. Setting Nurses’ Health Study. Participants 71 271 women enrolled in the Nurses’ Health Study residing throughout the contiguous United States who had valid estimates on exposure to particulate matter for at least one exposure period of interest and data on anxiety symptoms. Main Outcome Measures Meaningfully high symptoms of anxiety, defined as a score of 6 points or greater on the phobic anxiety subscale of the Crown-Crisp index, administered in 2004. results The 71 271 eligible women were aged between 57 and 85 years (mean 70 years) at the time of assessment of anxiety symptoms, with a prevalence of high anxiety symptoms of 15%. Exposure to particulate matter was characterized using estimated average exposure to particulate matter <2.5 ?m in diameter (PM2.5) and 2.5 to 10 ?m in diameter (PM2.5–10) in the one month, three months, six months, one year, and 15 years prior to assessment of anxiety symptoms, and residential distance to the nearest major road two years prior to assessment. Significantly increased odds of high anxiety symptoms were observed with higher exposure to PM2.5 for multiple averaging periods (for example, odds ratio per 10 ?g/m3 increase in prior one month average PM2.5: 1.12, 95% confidence interval 1.06 to 1.19; in prior 12 month average PM2.5: 1.15, 1.06 to 1.26). Models including multiple exposure windows suggested short term averaging periods were more relevant than long term averaging periods. There was no association between anxiety and exposure to PM2.5–10. Residential proximity to major roads was not related to anxiety symptoms in a dose dependent manner. Conclusions, exposure to fine particulate matter (PM2.5) was associated with high symptoms of anxiety, with more recent exposures potentially more relevant than more distant exposures. Research evaluating whether reductions in exposure to ambient PM2.5 would reduce the population level burden of clinically relevant symptoms of anxiety is warranted.

AUTISM SPECTRUM DISORDER IN RELATION TO DISTRIBUTION OF HAZARDAOUS AIR POLLUTANTS IN THE SAN FRANCISCO BAY AREA To explore possible associations between autism spectrum disorders (ASD) and environmental exposures, we linked the California autism surveillance system to estimated hazardous air pollutant (HAP) concentrations compiled by the U.S. Environmental Protection Agency. METHODS: Subjects included 284 children with ASD and 657 controls, born in 1994 in the San Francisco Bay area. We assigned exposure level by census tract of birth residence for 19 chemicals we identified as potential neurotoxicants, developmental toxicants, and/or endocrine disruptors from the 1996 HAPs database. Because concentrations of many of these were highly correlated, we combined the chemicals into mechanistic and structural groups, calculating summary index scores. We calculated ASD risk in the upper quartiles of these group scores or individual chemical concentrations compared with below the median, adjusting for demographic factors. RESULTS: The adjusted odds ratios (AORs) were elevated by 50% in the top quartile of chlorinated solvents and heavy metals 95% con?dence intervals (CIs), 1.1–2.1, but not for aromatic solvents. Adjusting for these three groups simultaneously led to decreased risks for the solvents and increased risk for metals (AORs for metals: fourth quartile = 1.7; 95% CI, 1.0–3.0; third quartile = 1.95; 95% CI, 1.2–3.1). The individual compounds that contributed most to these associations included mercury, cadmium, nickel, trichloroethylene, and vinyl chloride. CONCLUSIONS: Our results suggest a potential association between autism and estimated metal concentrations, and possibly solvents, in ambient air around the birth residence, requiring con?rmation and more re?ned exposure assessment in future studies.

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AIRBORNE CONCENTRATION OF PM2.5 AND DIESEL EXHAUST PARTICLES ON HARLEM SIDEWALKS: A COMMUNITY-BASED PILOT STUDY of the dense urban core neighborhoods of New York City (NYC) have expressed increasing concern about the potential human health impacts of diesel vehicle emissions. We measured concentrations of particulate matter less than or equal to 2.5 pm in aerodynamic diameter (PM25) and diesel exhaust particles (DEP) on el in Harlem, NYC, and tested whether spatial variations in concentrations were related to local diesel traffic density Eight-hour (1000-1800 hr.) air samples for PM2.5 and elemental carbon (EC) were collected for 5 days in July 1996 on sidewalk adjacent to four geographically distinct Harlem intersections. Samples were taken using portable monitors worn by study staff. Simultaneous traffic counts for diesel trucks, buses, car, and pedestrians were carried out at each intersection on greater than or equal to 2 of the 5 sampling days. Eight-hour diesel vehicle counts ranged from 61: to 2,467 across the four sites. Mean concentration of PM25 exhibited only modest site-to-site variation (3747 Nano grams/m3), reflecting the importance of broader regional sources ofPM2 5. In contrast, EC concentrations varied 4-fold across sites (from 1.5 to 6 Nano gram/m cube), and were associated with bus and truck counts on adjacent streets and, at one site, with the presence of a bus depot. A high correlation (r = 0.95) was observed between EC concentrations measured analytically and a blackness measurement based on PM25 filter and reflectance, suggesting the utility of the latter as a surrogate measure of DEP in future community-based studies. These results show that local diesel sources in Harlem create spatial variations in sidewalk concentrations of DEP. The study also demonstrates the feasibility of a new paradigm for community-based research involving full and active partnership between academic scientist and community-based organizations.THE ROLE OF PARTICLE COMPOSITION ON THE ASSOCIATION BETWEEN PM2.5 AND MORTALITY—Although the association between exposure to particulate matter (PM) mass and mortality is well established, there remains uncertainty about which chemical components of PM are most harmful to human health. Methods—A hierarchical approach was used to determine how the association between daily PM2.5 mass and mortality was modified by PM2.5 composition in 25 US communities. First, the association between daily PM2.5 and mortality was determined for each community and season using Poisson regression. Second, we used meta-regression to examine how the pooled association was modified by community and season-specific particle composition. Results—There was a 0.74% (95% confidence interval = 0.41%–1.07%) increase in nonaccidental deaths associated with a 10 ? g/m3 increase in 2-day averaged PM2.5 mass concentration. This association was smaller in the west (0.51% 0.10%– 0.92%) than in the east (0.92% 0.23%–1.36%), and was highest in spring (1.88% 0.23%–1.36%). It was increased when PM2.5 mass contained a higher proportion of aluminum (interquartile range = 0.58%), arsenic (0.55%), sulfate (0.51%), silicon (0.41%), and nickel (0.37%). The combination of aluminum, sulfate, and nickel also modified the effect. These species proportions explained residual variability between the community-specific PM2.5 mass effect estimates. Conclusions—this study shows that certain chemical species modify the association between PM2.5 and mortality and illustrates that mass alone is not a sufficient metric when evaluating health effects of PM exposure.

Portable direct-reading instruments by light-scattering method are increasingly used in airborne fine particulate matter (PM2.5) monitoring. However, there are limited calibration studies on such instruments by applying the gravimetric method as reference method in field tests.

Methods An 8-month sampling was performed and 96 pairs of PM2.5 data by both the gravimetric method and the simultaneous light-scattering real-time monitoring (QT-50) were obtained from July, 2015 to February, 2016 in Shanghai. Temperature and relative humidity (RH) were recorded. Mann-Whitney U nonparametric test and Spearman correlation were used to investigate the differences between the two measurements. Multiple linear regression (MLR) model was applied to set up the calibration model for the light-scattering device.

Results the average PM2.5 concentration (median) was 48.1?g/m3 (min-max 10.4–95.8?g/m3) by the gravimetric method and 58.1?g/m3 (19.2–315.9?g/m3) by the light-scattering method, respectively. By time trend analyses, they were significantly correlated with each other (Spearman correlation coefficient 0.889, P<0.01). By MLR, the calibration model for the light scattering instrument was Y (calibrated) = 57.45 + 0.47×X (the QT – 50 measurements) – 0.53×RH – 0.41×Temp with both RH and temperature adjusted. The 10-fold cross-validation R2 and the root mean squared error of the calibration model were 0.79 and 11.43?g/m3, respectively.

Light-scattering measurements of PM2.5 by QT-50 instrument overestimated the concentration levels and were affected by temperature and RH. The calibration model for QT-50 instrument was firstly set up against the gravimetric method with temperature and RH adjusted.