Dataset Information

Spatiotemporal Assessment of PM2.5-Related Economic Losses from Health Impacts during 2014?2016 in China.

ABSTRACT: Background: Particulate air pollution, especially PM2.5, is highly correlated with various adverse health impacts and, ultimately, economic losses for society, however, few studies have undertaken a spatiotemporal assessment of PM2.5-related economic losses from health impacts covering all of the main cities in China. Methods: PM2.5 concentration data were retrieved for 190 Chinese cities for the period 2014?2016. We used a log-linear exposure?response model and monetary valuation methods, such as value of a statistical life (VSL), amended human capital (AHC), and cost of illness to evaluate PM2.5-related economic losses from health impacts at the city level. In addition, Monte Carlo simulation was used to analyze uncertainty. Results: The average economic loss was 0.3% (AHC) to 1% (VSL) of the total gross domestic product (GDP) of 190 Chinese cities from 2014 to 2016. Overall, China experienced a downward trend in total economic losses over the three-year period, but the Beijing?Tianjin?Hebei, Shandong Peninsula, Yangtze River Delta, and Chengdu-Chongqing regions experienced greater annual economic losses. Conclusions: Exploration of spatiotemporal variations in PM2.5-related economic losses from long-term health impacts could provide new information for policymakers regarding priority areas for PM2.5 pollution prevention and control in China.

SUBMITTER: Yang Y

PROVIDER: S-EPMC6024949 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Spatiotemporal Assessment of PM2.5-Related Economic Losses from Health Impacts during 2014⁻2016 in China.

Yang Yang Y Luo Liwen L Song Chao C Yin Hao H Yang Jintao J

International journal of environmental research and public health 20180616 6

Background: Particulate air pollution, especially PM2.5, is highly correlated with various adverse health impacts and, ultimately, economic losses for society, however, few studies have undertaken a spatiotemporal assessment of PM2.5-related economic losses from health impacts covering all of the main cities in China. Methods: PM2.5 concentration data were retrieved for 190 Chinese cities for the period 2014⁻2016. We used a log-linear exposure⁻respo ...[more]

PMID: 29914184

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Project description:Elevated levels of ambient air pollution has been implicated as a major risk factor for morbidities and premature mortality in India, with particularly high concentrations of particulate matter in the Indo-Gangetic plain. High resolution spatiotemporal estimates of such exposures are critical to assess health effects at an individual level. This article retrospectively assesses daily average PM2.5 exposure at 1 km × 1 km grids in Delhi, India from 2010-2016, using multiple data sources and ensemble averaging approaches. We used a multi-stage modeling exercise involving satellite data, land use variables, reanalysis based meteorological variables and population density. A calibration regression was used to model PM2.5: PM10 to counter the sparsity of ground monitoring data. The relationship between PM2.5 and its spatiotemporal predictors was modeled using six learners; generalized additive models, elastic net, support vector regressions, random forests, neural networks and extreme gradient boosting. Subsequently, these predictions were combined under a generalized additive model framework using a tensor product based spatial smoothing. Overall cross-validated prediction accuracy of the model was 80% over the study period with high spatial model accuracy and predicted annual average concentrations ranging from 87 to 138 μg/m3. Annual average root mean squared errors for the ensemble averaged predictions were in the range 39.7-62.7 μg/m3 with prediction bias ranging between 4.6-11.2 μg/m3. In addition, tree based learners such as random forests and extreme gradient boosting outperformed other algorithms. Our findings indicate important seasonal and geographical differences in particulate matter concentrations within Delhi over a significant period of time, with meteorological and land use features that discriminate most and least polluted regions. This exposure assessment can be used to estimate dose response relationships more accurately over a wide range of particulate matter concentrations.

Project description:BackgroundThe respiratory system of children is vulnerable to exposure to particulate matter (PM) with a diameter of less than 2.5 and 10 μm (PM2.5 and PM10) or even lower.ObjectiveThis study assessed PM10 and PM2.5 levels and respiratory health impacts on children in schools located in an industrialized suburb in Kenya.MethodThe PM10 and PM2.5 levels were sampled from five public primary schools in Athi River Township and a control school during the wet and dry seasons. Outdoor and classroom samples were collected concurrently on an 8-hour mean during school hours on two consecutive days in each school and analyzed using gravimetric techniques. Five hundred and seventy-eight (n = 578) pupils aged 9-14 years from these schools were also evaluated for symptoms of respiratory illnesses and lung function using a questionnaire and spirometric method, respectively, during the same periods.ResultsIndoor median PM10 levels (μg/m3) ranged from 60.8-269.1 and 52.8-232.3 and PM2.5 values (μg/m3) of 17.7-52.4 and 28.5-75.5 during the dry and wet seasons, respectively. The control classrooms had significantly (p <0.05) lower median PM10 levels (μg/m3) of 5.2 and 4.2, and PM2.5 levels (μg/m3) of 3.5 and 3.0 during the respective seasons. Nearly all the classrooms in Athi River schools had PM2.5 and PM10 median levels that exceeded the World Health Organization (WHO) recommended levels. The indoor-to-outdoor ratios varied from 0.35-1.40 and 0.80-2.40 for PM10 and 0.30-0.80 and 0.80-1.40 for PM2.5 during the dry and wet seasons, respectively, suggesting higher levels in the classrooms during the wet season. The relative risk (RR) and odds ratio (OR) presented higher prevalence of respiratory diseases following PM exposure in all the Athi River schools than the control during the dry and wet seasons. At 95% CI, the RR and OR showed strong associations between high PM10 and PM2.5 levels and lung function deficits and vice versa. The association was more prevalent during the wet season.ConclusionsThe study calls for effective indoor air management programs in school environments to reduce PM exposure and respiratory health impacts.Participant consentObtained.Ethics approvalThe research permit and approvals were obtained from the University of Nairobi/Kenyatta National Hospital Ethics and Research Committee (KNH-UoN ERC Reference: P599/08/2016) and the National Commission for Science, Technology and Innovation (Reference: NACOSTI/P/18/4268/25724).Competing interestsThe authors declare no competing financial interests.

Dataset Information

Spatiotemporal Assessment of PM2.5-Related Economic Losses from Health Impacts during 2014?2016 in China.

Publications

Spatiotemporal Assessment of PM<sub>2.5</sub>-Related Economic Losses from Health Impacts during 2014⁻2016 in China.

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