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Development of land-use regression models for fine particles and black carbon in peri-urban South India
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Land-use regression (LUR) has been used to model local spatial
variability of particulate matter in cities of high-income
countries. Performance of LUR models is unknown in less
urbanized areas of low-/middle-income countries (LMICs)
experiencing complex sources of ambient air pollution and which
typically have limited land use data. To address these concerns,
we developed LUR models using satellite imagery (e.g.,
vegetation, urbanicity) and manually-collected data from a
comprehensive built-environment survey (e.g., roads, industries,
non-residential places) for a peri-urban area outside Hyderabad,
India. As part of the CHAI (Cardiovascular Health effects of Air
pollution in Telangana, India) project, concentrations of fine
particulate matter (PM2.5) and black carbon were measured over
two seasons at 23 sites. Annual mean (sd) was 34.1 (3.2)
mug/m(3) for PM2.5 and 2.7 (0.5) mug/m(3) for black carbon. The
LUR model for annual black carbon explained 78% of total
variance and included both local-scale (energy supply places)
and regional-scale (roads) predictors. Explained variance was
58% for annual PM2.5 and the included predictors were only
regional (urbanicity, vegetation). During leave-one-out
cross-validation and cross-holdout validation, only the black
carbon model showed consistent performance. The LUR model for
black carbon explained a substantial proportion of the spatial
variability that could not be captured by simpler interpolation
technique (ordinary kriging). This is the first study to develop
a LUR model for ambient concentrations of PM2.5 and black carbon
in a non-urban area of LMICs, supporting the applicability of
the LUR approach in such settings. Our results provide insights
on the added value of manually-collected built-environment data
to improve the performance of LUR models in settings with
limited data availability. For both pollutants, LUR models
predicted substantial within-village variability, an important
feature for future epidemiological studies.
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SANCHEZ, Margaux, et al. Development of land-use regression models for fine particles and
black carbon in peri-urban South India. The Science of the Total Environment. 2018. Vol. 634, num. 77-86. ISSN 0048-9697. [consulted: 15 of June of 2026]. Available at: https://hdl.handle.net/2445/121852