Dataset Information

Evaluation of an Air Quality Health Index for Predicting the Mutagenicity of Simulated Atmospheres.

ABSTRACT: No study has evaluated the mutagenicity of atmospheres with a calculated air quality health index (AQHI). Thus, we generated in a UV-light-containing reaction chamber two simulated atmospheres (SAs) with similar AQHIs but different proportions of criteria pollutants and evaluated them for mutagenicity in three Salmonella strains at the air-agar interface. We continuously injected into the chamber gasoline, nitric oxide, and ammonium sulfate, as well as either ?-pinene to produce SA-PM, which had a high concentration of particulate matter (PM): 119 ppb ozone (O3), 321 ppb NO2, and 1007 ?g/m3 PM2.5; or isoprene to produce SA-O3, which had a high ozone (O3) concentration: 415 ppb O3, 633 ppb NO2, and 55 ?g/m3 PM2.5. Neither PM2.5 extracts, NO2, or O3 alone, nor nonphoto-oxidized mixtures were mutagenic or cytotoxic. Both photo-oxidized atmospheres were largely direct-acting base-substitution mutagens with similar mutagenic potencies in TA100 and TA104. The mutagenic potencies [(revertants/h)/(mgC/m3)] of SA-PM (4.3 ± 0.4) and SA-O3 (9.5 ± 1.3) in TA100 were significantly different ( P < 0.0001), but the mutation spectra were not ( P = 0.16), being ?54% C ? T and ?46% C ? A. Thus, the AQHI may have some predictive value for the mutagenicity of the gas phase of air.

SUBMITTER: Zavala J

PROVIDER: S-EPMC5858694 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Evaluation of an Air Quality Health Index for Predicting the Mutagenicity of Simulated Atmospheres.

Zavala Jose J Krug Jonathan D JD Warren Sarah H SH Krantz Q Todd QT King Charly C McKee John J Gavett Stephen H SH Lewandowski Michael M Lonneman William A WA Kleindienst Tadeusz E TE Meier Matthew J MJ Higuchi Mark M Gilmour M Ian MI DeMarini David M DM

Environmental science & technology 20180220 5

No study has evaluated the mutagenicity of atmospheres with a calculated air quality health index (AQHI). Thus, we generated in a UV-light-containing reaction chamber two simulated atmospheres (SAs) with similar AQHIs but different proportions of criteria pollutants and evaluated them for mutagenicity in three Salmonella strains at the air-agar interface. We continuously injected into the chamber gasoline, nitric oxide, and ammonium sulfate, as well as either α-pinene to produce SA-PM, which had ...[more]

PMID: 29406743

Similar Datasets

Project description:ImportanceFuture changes in climate are likely to adversely affect human health by affecting concentrations of particulate matter sized less than 2.5 μm (PM2.5) and ozone (O3) in many areas. However, the degree to which these outcomes may be mitigated by reducing air pollutant emissions is not well understood.ObjectiveTo model the associations between future changes in climate, air quality, and human health for 2 climate models and under 2 air pollutant emission scenarios.Design, setting, and participantsThis modeling study simulated meteorological conditions over the coterminous continental US during a 1995 to 2005 baseline and over the 21st century (2025-2100) by dynamically downscaling representations of a high warming scenario from the Community Earth System Model (CESM) and the Coupled Model version 3 (CM3) global climate models. Using a chemical transport model, PM2.5 and O3 concentrations were simulated under a 2011 air pollutant emission data set and a 2040 projection. The changes in PM2.5 and O3-attributable deaths associated with climate change among the US census-projected population were estimated for 2030, 2050, 2075, and 2095 for each of 2 emission inventories and climate models. Data were analyzed from June 2018 to June 2020.Main outcomes and measuresThe main outcomes were simulated change in summer season means of the maximum daily 8-hour mean O3, annual mean PM2.5, population-weighted exposure, and the number of avoided or incurred deaths associated with these pollutants. Results are reported for 2030, 2050, 2075, and 2095, compared with 2000, for 2 climate models and 2 air pollutant emissions data sets.ResultsThe projected increased maximum daily temperatures through 2095 were up to 7.6 °C for the CESM model and 11.8 °C for the CM3 model. Under each climate model scenario by 2095, compared with 2000, an estimated additional 21 000 (95% CI, 14 000-28 000) PM2.5-attributable deaths and 4100 (95% CI, 2200-6000) O3-attributable deaths were projected to occur. These projections decreased to an estimated 15 000 (95% CI, 10 000-20 000) PM2.5-attributable deaths and 640 (95% CI, 340-940) O3-attributable deaths when simulated using a future emission inventory that accounted for reduced anthropogenic emissions.Conclusions and relevanceThese findings suggest that reducing future air pollutant emissions could also reduce the climate-driven increase in deaths associated with air pollution by hundreds to thousands.

Project description:ImportanceReducing exposure to fine particulate matter (<2.5 μm [PM2.5]) air pollution improves cardiopulmonary morbidity and mortality. However, the public health relevance of air quality index (AQI) activity guidelines under present-day environmental conditions in the US has not been critically assessed.ObjectiveTo evaluate the public health relevance of following PM2.5 AQI activity guidance in preventing serious atherosclerotic cardiovascular disease (ASCVD) and pulmonary events among adults in the US.Design, setting, and participantsThis cross-sectional modeling study involved the general adult population and sensitive individuals as designated by the US Environmental Protection Agency (EPA), including adults with preexisting ASCVD or lung disease (asthma or chronic obstructive pulmonary disease). The study was conducted between August 1, 2023, and January 31, 2024.ExposuresDaily AQI strata for PM2.5 and the corresponding activity recommendations.Main outcomes and measuresThe main outcome was the number needed to treat (NNT) per day by following activity guidance across daily AQI strata to prevent 1 serious ASCVD or pulmonary event among relevant populations. To calculate PM2.5-induced excess disease event rates per day, estimated baseline disease-specific daily event rates for each group were multiplied by the increase in risks due to PM2.5 levels at each AQI stratum. The number of events prevented per day was calculated by multiplying each excess disease event rate by the percentage in exposure reduction plausibly incurred by following population-specific activity guidance at each AQI level. The NNT is the reciprocal of the number of events prevented.ResultsThe NNT to prevent ASCVD events was high for the general population and for patients with ASCVD across all AQI strata. The range of values was comparatively lower to prevent pulmonary events among adults with lung disease. During most days (96%) when activity recommendations were promulgated due to elevated PM2.5 (AQI, 101-200), the NNT to prevent a serious disease event remained very high for the general population (>18 million), patients with ASCVD (approximately 1.6-5 million), and adults with lung disease (approximately 66 000-202 000).Conclusions and relevanceThese findings suggest that existing PM2.5 AQI activity recommendations are of questionable public health relevance in present-day conditions and merit consideration for updating to improve their potential effectiveness.

Project description:With the ongoing challenge of air pollution posing serious health and environmental threats, particularly in rapidly industrializing regions, accurate forecasting and effective pollutant identification are crucial for enhancing public health and ecological stability. This study aimed to optimize air quality management through the prediction of the Air Quality Index (AQI) and identification of air pollutants. Our study spans nine representative cities (Hohhot, Yinchuan, Lanzhou, Beijing, Taiyuan, Xi'an, Shanghai, Nanjing, Wuhan) in China, with data collected from January 1, 2015, to November 30, 2021. We proposed a new model for daily AQI prediction, termed VMD-CSA-CNN-LSTM, which employed advanced machine learning techniques, including convolutional neural networks (CNN) and long short-term memory (LSTM) networks, and leveraged the chameleon swarm algorithm (CSA) for hyperparameter optimization, integrated through a variational mode decomposition approach. The model was developed using data from Lanzhou, with a split ratio of 8:1:1 into training, validation, and test sets, achieving an RMSE of 2.25, MAPE of 0.02, adjusted R-squared of 98.91%, and training efficiency of 5.31%. The model was further externally validated in the other eight cities, yielding comparable results, with an adjusted R-squared above 96%, MAPE below 0.1, and RMSE below 7.5. Additionally, we employed a random forest algorithm to identify the primary pollutants contributing to AQI levels. Our results indicated that PM2.5 was the most significant pollutant in Beijing, Taiyuan, and Xi'an, while PM10 was dominant in Hohhot, Yinchuan, and Lanzhou. In Shanghai, Nanjing, and Wuhan, both PM2.5 and PM10 were critical, with ozone also identified as a major air pollutant. This study not only advances the predictive accuracy of AQI models but also aids policymakers by providing a reliable tool for air quality management and strategic planning aimed at pollution reduction. The integration of these advanced computational techniques into environmental monitoring practices offers a promising avenue for enhancing air quality and mitigating pollution-related risks.

Dataset Information

Evaluation of an Air Quality Health Index for Predicting the Mutagenicity of Simulated Atmospheres.

Publications

Evaluation of an Air Quality Health Index for Predicting the Mutagenicity of Simulated Atmospheres.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets