Sensitivity of Air Pollution Exposure and Disease Burden to Emission Changes in China Using Machine Learning Emulation.
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SUBMITTER: Conibear L
PROVIDER: S-EPMC9207901 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Data-driven machine learning methods present an opportunity to simultaneously assess the impact of multiple air pollutants on health outcomes. The goal of this study was to apply a two-stage, data-driven approach to identify associations between air pollutant exposure profiles and children's cognitive skills. Data from 6900 children enrolled in the Early Childhood Longitudinal Study, Birth Cohort, a national study of children born in 2001 and followed through kindergarten, were linked to estimated concentrations of 104 ambient air toxics in the 2002 National Air Toxics Assessment using ZIP code of residence at age 9 months. In the first-stage, 100 regression trees were learned to identify ambient air pollutant exposure profiles most closely associated with scores on a standardized mathematics test administered to children in kindergarten. In the second-stage, the exposure profiles frequently predicting lower math scores were included within linear regression models and adjusted for confounders in order to estimate the magnitude of their effect on math scores. This approach was applied to the full population, and then to the populations living in urban and highly-populated urban areas. Our first-stage results in the full population suggested children with low trichloroethylene exposure had significantly lower math scores. This association was not observed for children living in urban communities, suggesting that confounding related to urbanicity needs to be considered within the first-stage. When restricting our analysis to populations living in urban and highly-populated urban areas, high isophorone levels were found to predict lower math scores. Within adjusted regression models of children in highly-populated urban areas, the estimated effect of higher isophorone exposure on math scores was -1.19 points (95% CI -1.94, -0.44). Similar results were observed for the overall population of urban children. This data-driven, two-stage approach can be applied to other populations, exposures and outcomes to generate hypotheses within high-dimensional exposure data.
| S-EPMC5595640 | biostudies-literature
Project description:BackgroundWhile a few studies have tried to estimate the economic burden of noncommunicable diseases (NCDs) associated with air pollution, most previous studies have methodological limitations. For example, neither the cost of illness approach nor the value of a statistical life approach accounts for economic adjustment mechanisms (i.e., they do not include substitution of labor lost due to an illness with capital or other workers), and neither approach considers disease impact on physical and human capital. Furthermore, since new evidence shows that air pollution is also linked to diabetes, previous studies did not estimate the economic costs of diabetes associated with air pollution. The total economic costs of NCDs associated with air pollution under a comprehensive framework therefore remained unexplored.ObjectivesThis study uses a human capital-augmented production function framework to analyze and estimate the macroeconomic impact of NCDs associated with air pollution in China in 1990-2030 and in 2015-2030. It makes several contributions-beyond those of the extant literature-to understanding the economic burden of NCDs associated with air pollution. It does this by accounting for economic adjustment mechanisms and by incorporating human capital into the model.MethodsIn our framework, aggregate output is produced according to a human capital-augmented production function that accounts for the effects of projected disease prevalence. NCDs associated with air pollution affect the aggregate output through three pathways: 1) Mortality effect-when working-age individuals die from a disease, aggregate output decreases because physical capital is an imperfect substitute for the loss of human capital in the production process. 2) Morbidity effect-when working-age individuals suffer from a disease but do not die from it, their contribution to overall output also decreases depending on disease severity; for example, they might work fewer hours or with lower productivity, or they might retire earlier. We also incorporate age-specific human capital to account for education-related productivity differences between members of different cohorts who are differentially affected by NCDs. 3) Treatment cost effect-when households in which members suffer from a disease use part of their savings to cover the out-of-pocket share of their treatment costs, physical capital accumulation diminishes. Our estimates are based on the recently updated Global Burden of Disease epidemiology data, which identify four pathways through which air pollution affects health: cardiovascular diseases, respiratory diseases, cancer, and diabetes.ResultsTotal losses from NCDs associated with air pollution in China in 1990-2030 are estimated to be $1,137 billion (constant 2010 USD) and in 2015-2030 are estimated to be $499 billion (constant 2010 USD). Cardiovascular diseases account for the highest burden, followed by chronic respiratory diseases, diabetes, and cancer. Treatment costs account for nearly 30% of the total economic burden of NCDs associated with air pollution. We also find that the share of economic burden associated with treatment costs is highest for diabetes. This is mainly driven by the fact that, on a per case basis, diabetes has a lower health burden than other diseases associated with air pollution.DiscussionThe NCDs associated with air pollution impose a large economic burden on China.
| S-EPMC6472813 | biostudies-literature
Project description:Few studies have investigated the chronic obstructive pulmonary disease (COPD) mortality fraction attributable to air pollution and modification by individual characteristics of air pollution effects. We applied distributed lag non-linear models to assess the associations between air pollution and COPD mortality in 2007-2011 in Guangzhou, China, and the total COPD mortality fraction attributable to air pollution was calculated as well. We found that an increase of 10??g/m(3) in particulate matter with an aerodynamic diameter of 10??m or less (PM10), sulfur dioxide (SO2) and nitrogen dioxide (NO2) was associated with a 1.58% (95% confidence interval (CI): 0.12-3.06%), 3.45% (95% CI: 1.30-5.66%) and 2.35% (95% CI: 0.42-4.32%) increase of COPD mortality over a lag of 0-15 days, respectively. Greater air pollution effects were observed in the elderly, males and residents with low educational attainment. The results showed 10.91% (95% CI: 1.02-9.58%), 12.71% (95% CI: 5.03-19.85%) and 13.38% (95% CI: 2.67-22.84%) COPD mortality was attributable to current PM10, SO2 and NO2 exposure, respectively. In conclusion, the associations between air pollution and COPD mortality differed by individual characteristics. There were remarkable COPD mortality burdens attributable to air pollution in Guangzhou.
| S-EPMC4872534 | biostudies-literature
Project description:Air pollution can increase the risk of respiratory diseases, enhancing the susceptibility to viral and bacterial infections. Some studies suggest that small air particles facilitate the spread of viruses and also of the new coronavirus, besides the direct person-to-person contagion. However, the effects of the exposure to particulate matter and other contaminants on SARS-CoV-2 has been poorly explored. Here we examined the possible reasons why the new coronavirus differently impacted on Italian regional and provincial populations. With the help of artificial intelligence, we studied the importance of air pollution for mortality and positivity rates of the SARS-CoV-2 outbreak in Italy. We discovered that among several environmental, health, and socio-economic factors, air pollution and fine particulate matter (PM2.5), as its main component, resulted as the most important predictors of SARS-CoV-2 effects. We also found that the emissions from industries, farms, and road traffic - in order of importance - might be responsible for more than 70% of the deaths associated with SARS-CoV-2 nationwide. Given the major contribution played by air pollution (much more important than other health and socio-economic factors, as we discovered), we projected that, with an increase of 5-10% in air pollution, similar future pathogens may inflate the epidemic toll of Italy by 21-32% additional cases, whose 19-28% more positives and 4-14% more deaths. Our findings, demonstrating that fine-particulate (PM2.5) pollutant level is the most important factor to predict SARS-CoV-2 effects that would worsen even with a slight decrease of air quality, highlight that the imperative of productivity before health and environmental protection is, indeed, a short-term/small-minded resolution.
| S-EPMC7442434 | biostudies-literature
Project description:In this study, we modeled early life air pollution exposure using C57BL/6J male mice on a controlled chow diet, exposed to real-world inhaled concentrated PM2.5 (~10x ambient level/ ~60-120g/m3) or filtered air (FA) over 14 weeks. We investigated PM2.5 effects on phenotype, transcriptome and chromatin accessibility, compared the effects with a prototypical high-fat diet (HFD) stimulus, and examined the effects of cessation of exposure on reversibility of phenotype/genotype.
2020-11-05 | GSE145840 | GEO
Project description:Urban air pollution has aroused growing attention due to its associated adverse health effects. A model which could promptly predict urban air quality with considerable accuracy is, therefore, important and will benefit the development of smart cities. However, only a computational fluid dynamics (CFD) model could better resolve the dispersion behavior within an urban canyon layer. A machine learning (ML) model using the Artificial Neural Network (ANN) approach was formulated in the current study to investigate vehicle-derived airborne particulate (PM10) dispersion within a compact high-rise-built environment. Various measured meteorological parameters and PM10 concentrations were adopted as the model inputs to train the ANN model. A building-resolved CFD model under the same environmental settings was also set up to compare its model performance with the ANN model. Our results showed that the ANN model exhibited promising performance (r = 0.82, fractional bias = 0.002) when comparing the > 1000 h PM10 measurements. When comparing the diurnal hourly measured PM10 variations in a clear-sky day, both the ANN and CFD models performed well (r > 0.8). The good performance of the CFD model relied on the knowledge of the in situ diurnal traffic profile, the adoption of suitable mobile source emission factor(s) (e.g., from MOBILE 6 and COPERT4), and the use of urban thermal and dynamical variables to capture PM10 variations in both neutral and unstable atmospheric conditions. These requirements/constraints make it impractical for daily operation. On the contrary, the ML (ANN) model adopted here is free from these constraints and is fast (less than 0.1% computational time relative to the CFD model). These results demonstrate that the ANN model is a superior option for a smart city application.
| S-EPMC9915966 | biostudies-literature
Project description:BACKGROUND:Socioeconomically disadvantaged populations often have higher exposures to particulate air pollution, which can be expected to contribute to differentials in life expectancy. We examined socioeconomic differentials in exposure and air pollution-related mortality relating to larger scale (5 km resolution) variations in background concentrations of selected pollutants across England. METHODS:Ozone and particulate matter (sub-divided into PM10, PM2.5, PM2.5-10, primary, nitrate and sulphate PM2.5) were simulated at 5 km horizontal resolution using an atmospheric chemistry transport model (EMEP4UK). Annual mean concentrations of these pollutants were assigned to all 1,202,578 residential postcodes in England, which were classified by urban-rural status and socioeconomic deprivation based on the income and employment domains of the 2010 English Index of Multiple Deprivation for the Lower-level Super Output Area of residence. We used life table methods to estimate PM2.5-attributable life years (LYs) lost in both relative and absolute terms. RESULTS:Concentrations of the most particulate fractions, but not of nitrate PM2.5 or ozone, were modestly higher in areas of greater socioeconomic deprivation. Relationships between pollution level and socioeconomic deprivation were non-linear and varied by urban-rural status. The pattern of PM2.5 concentrations made only a small contribution to the steep socioeconomic gradient in LYs lost due to PM2.5 per 103 population, which primarily was driven by the steep socioeconomic gradient in underlying mortality rates. In rural areas, the absolute burden of air pollution-related LYs lost was lowest in the most deprived deciles. CONCLUSIONS:Air pollution shows modest socioeconomic patterning at 5 km resolution in England, but absolute attributable mortality burdens are strongly related to area-level deprivation because of underlying mortality rates. Measures that cause a general reduction in background concentrations of air pollution may modestly help narrow socioeconomic differences in health.
| S-EPMC6389046 | biostudies-literature
Project description:Ambient air pollution is associated with numerous adverse health impacts. Previous assessments of global attributable disease burden have been limited to urban areas or by coarse spatial resolution of concentration estimates. Recent developments in remote sensing, global chemical-transport models, and improvements in coverage of surface measurements facilitate virtually complete spatially resolved global air pollutant concentration estimates. We combined these data to generate global estimates of long-term average ambient concentrations of fine particles (PM(2.5)) and ozone at 0.1° × 0.1° spatial resolution for 1990 and 2005. In 2005, 89% of the world's population lived in areas where the World Health Organization Air Quality Guideline of 10 ?g/m(3) PM(2.5) (annual average) was exceeded. Globally, 32% of the population lived in areas exceeding the WHO Level 1 Interim Target of 35 ?g/m(3), driven by high proportions in East (76%) and South (26%) Asia. The highest seasonal ozone levels were found in North and Latin America, Europe, South and East Asia, and parts of Africa. Between 1990 and 2005 a 6% increase in global population-weighted PM(2.5) and a 1% decrease in global population-weighted ozone concentrations was apparent, highlighted by increased concentrations in East, South, and Southeast Asia and decreases in North America and Europe. Combined with spatially resolved population distributions, these estimates expand the evaluation of the global health burden associated with outdoor air pollution.
| S-EPMC4043337 | biostudies-literature
Project description:While recent assessments have quantified the burden of air pollution at the national scale in China, air quality managers would benefit from assessments that disaggregate health impacts over regions and over time. We took advantage of a new 10×10km satellite-based PM2.5 dataset to analyze spatial and temporal trends of air pollution health impacts in China, from 2004 to 2012. Results showed that national PM2.5 related deaths from stroke, ischemic heart disease and lung cancer increased from approximately 800,000 cases in 2004 to over 1.2 million cases in 2012. The health burden exhibited strong spatial variations, with high attributable deaths concentrated in regions including the Beijing-Tianjin Metropolitan Region, Yangtze River Delta, Pearl River Delta, Sichuan Basin, Shandong, Wuhan Metropolitan Region, Changsha-Zhuzhou-Xiangtan, Henan, and Anhui, which have heavy air pollution, high population density, or both. Increasing trends were found in most provinces, but with varied growth rates. While there was some evidence for improving air quality in recent years, this was offset somewhat by the countervailing influences of in-migration together with population growth. We recommend that priority areas for future national air pollution control policies be adjusted to better reflect the spatial hotspots of health burdens. Satellite-based exposure and health impact assessments can be a useful tool for tracking progress on both air quality and population health burden reductions.
| S-EPMC5479577 | biostudies-literature
Project description:BackgroundAir pollution is a major risk factor for planetary health and has long been suspected of predisposing humans to respiratory diseases induced by pathogens like influenza viruses. However, epidemiological evidence remains elusive due to lack of longitudinal data from large cohorts.ObjectiveOur aim is to quantify the short-term association of influenza incidence with exposure to ambient air pollutants in Chinese cities.MethodsBased on air pollutant data and influenza surveillance data from 82 cities in China over a period of 5 years, we applied a two-stage time series analysis to assess the association of daily incidence of reported influenza cases with six common air pollutants [particulate matter with aerodynamic diameter ≤2.5μm (PM2.5), particulate matter with aerodynamic diameter ≤10μm (PM10), NO2, SO2, CO, and O3], while adjusting for potential confounders including temperature, relative humidity, seasonality, and holiday effects. We built a distributed lag Poisson model for one or multiple pollutants in each individual city in the first stage and conducted a meta-analysis to pool city-specific estimates in the second stage.ResultsA total of 3,735,934 influenza cases were reported in 82 cities from 2015 to 2019, accounting for 72.71% of the overall case number reported in the mainland of China. The time series models for each pollutant alone showed that the daily incidence of reported influenza cases was positively associated with almost all air pollutants except for ozone. The most prominent short-term associations were found for SO2 and NO2 with cumulative risk ratios of 1.094 [95% confidence interval (CI): 1.054, 1.136] and 1.093 (95% CI: 1.067, 1.119), respectively, for each 10 μg/m3 increase in the concentration at each of the lags of 1-7 d. Only NO2 showed a significant association with the daily incidence of influenza cases in the multipollutant model that adjusts all six air pollutants together. The impact of air pollutants on influenza was generally found to be greater in children, in subtropical cities, and during cold months.DiscussionIncreased exposure to ambient air pollutants, particularly NO2, is associated with a higher risk of influenza-associated illness. Policies on reducing air pollution levels may help alleviate the disease burden due to influenza infection. https://doi.org/10.1289/EHP12146.
| S-EPMC10711743 | biostudies-literature