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Use of spatiotemporal characteristics of ambient PM_2.5 in rural South India to infer local versus regional contributions.

ABSTRACT: This study uses spatiotemporal patterns in ambient concentrations to infer the contribution of regional versus local sources. We collected 12 months of monitoring data for outdoor fine particulate matter (PM_2.5) in rural southern India. Rural India includes more than one-tenth of the global population and annually accounts for around half a million air pollution deaths, yet little is known about the relative contribution of local sources to outdoor air pollution. We measured 1-min averaged outdoor PM_2.5 concentrations during June 2015-May 2016 in three villages, which varied in population size, socioeconomic status, and type and usage of domestic fuel. The daily geometric-mean PM_2.5 concentration was ∼30 μg m^-3 (geometric standard deviation: ∼1.5). Concentrations exceeded the Indian National Ambient Air Quality standards (60 μg m^-3) during 2-5% of observation days. Average concentrations were ∼25 μg m^-3 higher during winter than during monsoon and ∼8 μg m^-3 higher during morning hours than the diurnal average. A moving average subtraction method based on 1-min average PM_2.5 concentrations indicated that local contributions (e.g., nearby biomass combustion, brick kilns) were greater in the most populated village, and that overall the majority of ambient PM_2.5 in our study was regional, implying that local air pollution control strategies alone may have limited influence on local ambient concentrations. We compared the relatively new moving average subtraction method against a more established approach. Both methods broadly agree on the relative contribution of local sources across the three sites. The moving average subtraction method has broad applicability across locations.

SUBMITTER: Kumar MK

PROVIDER: S-EPMC5980999 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Use of spatiotemporal characteristics of ambient PM_2.5 in rural South India to infer local versus regional contributions.

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Dataset Information

Use of spatiotemporal characteristics of ambient PM2.5 in rural South India to infer local versus regional contributions.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

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Use of spatiotemporal characteristics of ambient PM_2.5 in rural South India to infer local versus regional contributions.