# name
Development of land-use regression models for fine particles and
                black carbon in peri-urban South India

# metadata.dc.description.abstract[].value
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.

# metadata.dc.relation.isformatof[].value
Reproducció del document publicat a:
                http://dx.doi.org/10.1016/j.scitotenv.2018.03.308

# metadata.dc.relation.ispartof[].value
The Science of the Total Environment, 2018, vol. 634, p.
                77-86

# metadata.dc.source[].value
Articles publicats en revistes (ISGlobal)

# metadata.dc.title[].value
Development of land-use regression models for fine particles and
                black carbon in peri-urban South India