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In this study, we present long-term near-surface measurements of sulfur dioxide (SO 2 ), oxides of nitrogen (NO x ), carbon monoxide (CO), and ozone (O 3 ) carried out at an urban location, Kanpur (26.46°N, 80.33°E, 125 m amsl), in Northern India from June 2009 to May 2013. The mean concentrations of SO 2 , NO x , CO, and O 3 over the entire study period were 3.0, 5.7, 721, and 27.9 ppb, respectively. SO 2 , NO x and CO concentrations were highest during the winter season, whereas O 3 concentration peaked during summer. The former could be attributed mainly to the near-surface anthropogenic sources (e.g. automobiles, residential cooking, brick kilns, coal-burning power plants, agricultural land-clearing, and biomass burning) and low mixing height in winter, whereas the latter was clearly due to enhanced chemical production of O 3 during the pre-monsoon (i.e. summer) season. The lowest concentration of all trace gases were observed during the monsoon season, due to efficient wet scavenging by precipitation. The averaged diurnal patterns also showed similar seasonal variation. NO x and CO showed peaks during morning and evening traffic hours and a valley in the afternoon irrespective of the seasons, clearly linked to the boundary layer height evolution. Contrarily, O 3 depicted a reverse pattern with highest concentrations during afternoon hours and lowest in the morning hours. The mean rate of change of O 3 concentrations (dO 3 /dt) during the morning hours (08:00 to 11:00 h) and evening hours (17:00 to 19:00 h) at Kanpur were 3.3 ppb h −1 and −2.6 ppb h −1 , respectively. O 3 followed a positive linear relationship with temperature, except in post-monsoon season while the strong negative with the relative humidity in all seasons. The ventilation coefficient was found to be highest in the pre-monsoon season (15,622 m 2 s −1 ) and lowest during winter (2564 m 2 s −1 ), indicative of excellent pollution dispersion efficiency during the pre-monsoon season. However, the low ventilation coefficient during winter and post-monsoon seasons indicated that the highpollution potential occurs at this site.

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Delhi is one among the highly air polluted cities in the world. Absence of causal relationship between emitting sources of PM and their impact has resulted in inadequate actions. This research combines a set of innovative and state-of-the-art analytical techniques to establish relative predominance of PM sources. Air quality sampling at six sites in summer and winter for 40 days (at each site) showed alarmingly high PM concentrations (340 ± 135 μg/m). The collected PM was subjected to chemical speciation including ions, metals, organic and elemental carbons which followed application of chemical mass balance technique for source apportionment. The source apportionment results showed that secondary aerosols, biomass burning (BMB), vehicles, fugitive dust, coal and fly ash, and municipal solid waste burning were the important sources. It was observed that secondary aerosol and crustal matter accounted for over 50% of mass. The PM levels were not solely result of emissions from Delhi; it is a larger regional problem caused by contiguous urban agglomerations. It was argued that emission reduction of precursors of secondary aerosol, SO, NO, and volatile organic compounds, which are unabated, is essential. A substantial reduction in BMB and suspension of crustal dust is equally important to ensure compliance with air quality standards.

The Industrial Source Complex Short Term (ISCST3) model was used to discern the sources responsible for high PM 10 levels in Kanpur City, a typical urban area in the Ganga basin, India. A systematic geographic information system-based emission inventory was developed for PM 10 in each of 85 grids of 2×2 km. The total emission of PM 10 was estimated at 11 t day −1 with an overall breakup as follows: (a) industrial point sources, 2.9 t day −1 (26%); (b) vehicles, 2.3 t day −1 (21%); (c) domestic fuel burning, 2.1 t day −1 (19%); (d) paved and unpaved road dust, 1.6 t day −1 (15%); and the rest as other sources. To validate the ISCST3 model and to assess air-quality status, sampling was done in summer and winter at seven sampling sites for over 85 days; PM 10 levels were very high (89-632 μg m −3 ). The results show that the model-predicted concentrations are in good agreement with observed values, and the model performance was found satisfactory. The validated model was run for each source on each day of sampling. The overall source contribution to ambient air pollution was as follows: vehicular traffic (16%), domestic fuel uses (16%), paved and unpaved road dust (14%), and industries (7%). Interestingly, the largest point source (coal-based power plant) did not contribute significantly to ambient air pollution. The reason might be due to release of pollutant at high stack height. The ISCST3 model was shown to produce source apportionment results like receptor modeling that could generate source apportionment results at any desired time and space resolution.