ABSTRACT
Concentrations of ambient particulate matter (PM) depend on various factors including emissions of primary pollutants, meteorology and chemical transformations. New Delhi, India is the most polluted megacity in the world and routinely experiences extreme pollution episodes. As part of the Delhi Aerosol Supersite study, we measured online continuous PM1 (particulate matter of size less than 1µm) concentrations and composition for over five years starting January 2017, using an Aerosol Chemical Speciation Monitor (ACSM). Here, we describe the development and application of machine learning models using random forest regression to estimate the concentrations, composition, sources and dynamics of PM in Delhi. These models estimate PM1 species concentrations based on meteorological parameters including ambient temperature, relative humidity, planetary boundary layer height, wind speed, wind direction, precipitation, agricultural burning fire counts, solar radiation and cloud cover. We used hour of day, day of week and month of year as proxies for time-dependent emissions (e.g., emissions from traffic during rush hours). We demonstrate the applicability of these models to capture temporal variability of the PM1 species, to understand the influence of individual factors via sensitivity analyses, and to separate impacts of the COVID-19 lockdowns and associated activity restrictions from impacts of other factors. Our models provide new insights into the factors influencing ambient PM1 in New Delhi, India, demonstrating the power of machine learning models in atmospheric science applications. [ABSTRACT FROM AUTHOR] Copyright of Aerosol Science & Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
ABSTRACT
The effects of the urban morphological characteristics on the spatial variation of near-surface PM2.5 air quality were examined. Unlike previous studies, we performed the analyses in real urban environments using continuous observations covering the whole scale of urban densities typically found in cities. We included data from 31 measurement stations divided into 8 different wind sectors with individually defined morphological characteristics leading to highly varying urban characteristics. The urban morphological characteristics explained up to 73% of the variance in normalized PM2.5 concentrations in street canyons, indicating that the spatial variation of the near-surface PM2.5 air quality was mostly defined by the characteristics studied. The fraction of urban trees nearby the stations was found to be the most important urban morphological characteristic in explaining the PM2.5 air quality, followed by the height-normalized roughness length as the second important parameter. An increase in the fraction of trees within 50 m of the stations from 25 percentile to 75 percentile (i.e. by the interquartile range, IQR) increased the normalized PM2.5 concentration by up to 24% in the street canyons. In open areas, an increase in the trees by the IQR actually decreased the normalized PM2.5 by 6% during the pre-COVID period. An increase in the height-normalized roughness length by the IQR increased the normalized PM2.5 by 9% in the street canyons. The results obtained in this study can help urban planners to identify the key urban characteristics affecting the near-surface PM2.5 air quality and also help researchers to evaluate how representative the existing measurement stations are compared to other parts of the cities.