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Chemical characterization and source apportionment of PM10 and PM2.5 were carried out for two different elevations (lower elevation (LE) ~ 5–10 m and higher elevation (HE) ~ 30–45 m) at four different locations representing urban background, city center, upwind, and downwind of the Delhi city during January 2017–March 2017. The 24-h average PM10 and PM2.5 concentrations were varied between 135.2–258.7 and 79.3–120.9 µg/m3, respectively. The average PM10 and PM2.5 concentrations were found significantly higher at LE than HE. The PM samples were analyzed for ions, elements and carbonaceous matter (EC/OC), and their concentrations (except S, V, As, Ni, Sb, Sr, Ga, elements associated with industrial combustion activities, and NO3−, attributed to high nitrate formation potential at HE) were observed higher in LE than HE at all the study locations. The chemical mass balance model was applied to quantify the source contributions to PM10 and PM2.5 mass at two different elevations. Model identified vehicular emission (diesel, PM10 ~ 8.8–21.7% and PM2.5 ~ 10.5–24.4% and gasoline, PM10 ~ 4.8–15.6% and PM2.5 ~ 6.7–14.8%), industrial residual oil combustion (PM10 ~ 8.8–23.5% and PM2.5 ~ 3.2–10.4%), road dust (PM10 ~ 13.6–22.3% and PM2.5 ~ 8.8–17.8%), soil dust (PM10 ~ 33.8–41.1% and PM2.5 ~ 5.8–8.3%), secondary nitrate (PM10 ~ 6.1–16.2% and PM2.5 ~ 13.4–20.2%), secondary sulfate (PM10 ~ 7.1–12.3% and PM2.5 ~ 10.6–16.7%), and biomass burning (PM10 ~ 6.8–21.8% and PM2.5 ~ 4.9–38.7%) as the main sources of PM10 and PM2.5 mass at both the elevations at all the study sites. The contribution of industrial residual oil combustion, vehicular emission, and secondary nitrate to PM10 and PM2.5 mass was found relatively higher in HE than LE. Results also revealed that biomass burning contributed significantly to PM pollution in the outskirts of Delhi than inside the city. Further, potential source contribution function analysis revealed that there may not be a long-range transport of PM emitted from biomass burning in the upwind region of Delhi during the study period. Shifting to Indian BS VI vehicles and fuel, switching to cleaner fuel in slum households, strict compliance on industries, and regular vacuum cleaning of roads will reduce the severe air quality problem in Delhi.

Health risk associated with air pollution and its exposure has gained much importance in the recent years. The potential toxicity of air pollutants especially particulate matter (PM) is attributed to its chemical composition and size. Hence, it is imperative to understand the morphology, size, and chemical composition of PM for health risk assessment. Polyaromatic hydrocarbons (PAHs) present in PM have high toxicity and mutagenic potential. Recent advancement in technology such as bioassays for cytotoxicity, oxidative stress, and apoptosis studies using human cell lines has allowed us to comprehend the underlying mechanism of particle toxicity and its impact on human health. This chapter deals with the health risks associated with air pollution and exposure assessment with a focus on chemical toxicity associated with air pollutants, especially PM. Health risks associated with particle-bound chemical compounds in school children are also explained in this chapter.

Traffic-related emissions are one of the major sources of ambient air pollution in many cities. Personal exposure to traffic-related air pollution is major concern in many cities of both developed and developing countries. In developing nations such as India, trade-off between urban transportation and air quality is always a hurdle considering the complex road networks, heterogeneous traffic, and increasing transportation demand. This chapter discusses the characteristics of traffic-related exhaust and nonexhaust air pollution in the urban areas and its personal exposure, health impacts as well as some of the market solutions to reduce the personal exposure to vehicular air pollution.

Personal exposure at sidewalks along urban corridors is a topical issue which has been rarely touched upon in great detail. In the present study, statistical distribution models and particle dosimetry studies were integrated to characterize the short-term personal exposure at sidewalks along major urban corridors. The size segregated PM measurements were carried out at two contrasting urban corridors (IT corridor and beach promenade) during morning and evening peak traffic hours. The PM concentrations at IT corridor was found to be highly dependent on traffic volume and meteorology, while at beach promenade the beach activities were found to have a significant role in determining PM concentration. The PM data were fitted with the probability distribution functions (PDF) for the assessment of frequency ranges experienced by pedestrians. At both the sidewalks, the PM10 and PM2.5 mass concentrations were fitted with loglogistic-3P model while PM1 data was found to best fit to general extreme value distribution. The particle dosimetry assessment showed higher PM2.5 lung deposition in IT corridor during the morning hours (7.25 μg) as compared to the evening hours (3.6 μg). At beach promenade higher PM2.5 lung deposition was significant during the evening hours (7.14 μg) as compared to the morning hours (2.4 μg). The present method is useful for the determination of short-term peak exposures to PM at urban hotspots.

This paper presents the characterization of air quality monitored at near field region (NFR) and far field region (FFR) of a national highway located at an industrial complex. The pollutants such as PM 10 , SO 2 and NO 2 were monitored in two campaigns (11th September to 18th October 2012 and 18th January to 17th February 2013). The 24 h average PM 10 concentration at NFR and FFR were found to be 86.69 ± 18.56 μg/m 3 ; 73.16 ± 16.21 μg/m 3 and 89.44 ± 18.69 μg/m 3 ; 81.91 ± 16.42 μg/m 3 , respectively during first and second campaign. In both the campaigns PM 10 , SO 2 and NO 2 concentration at NFR was higher than FFR. The chemical characterization of PM 10 at NFR and FFR indicated the abundance of major elements such as Na (NFR = 30% and FFR = 32%), Ca (NFR = 12% and FFR = 14%) and ions namely NO 3 − (NFR = 71% and FFR = 68%) and NH 3 + (NFR = 15% and FFR = 19%). Further, at FFR, SO 4 2 − and NO 3 − were found to be 18% and 35% higher than NFR indicating the conversions of SO 2 and NO 2 concentration into secondary particles. The measured SO 2 and NO 2 concentrations were 23 and 21% lower at FFR when compared to NFR confirms the secondary formation. The CALPUFF, EPA regulatory model was set up to understand the dynamics of air pollutants at the industrial complex. The predicted PM 10 , SO 2 and NO 2 concentrations at NFR and FFR were found to be 32.31 ± 1.56 μg/m 3 and 31.35 ± 1.27 μg/m 3 ; 0.37 ± 0.21 μg/m 3 and 0.06 ± 0.04 μg/m 3 ; 12.83 ± 6.55 μg/m 3 and 4.67 ± 2.77 μg/m 3 , respectively. The model showed moderate predictions for PM 10 (R 2 = 0.44–0.52), SO 2 (R 2 = 0.41–0.51) and NO 2 (R 2 = 0.45–0.61) concentrations.

Delhi National Capital Region (Delhi NCR) is facing serious challenges linked to worrying levels of air pollution (mainly NO2, PM10 and PM2.5). The CADTIME prject (Clean Air in Delhi through Implementation, Mitigation and Engagement) aims to understand what is required to deliver significant reductions in levels of air pollution. This paper presents the results of the first stage of the project: it firstly contextualises the challenges of air quality management in Delhi within the broader evolution of environmental policies and governance in India, with particular consideration to the tensions between environmental protection and the country’s development objectives. Secondly, it sets out how CADTIME will combine multiple source qualitative and quantitative data to develop an air quality action plan and an implementation strategy. In particular, through two workshops with local and national experts and stakeholders, and two rounds of focus groups with citizens of Delhi we will contrast stakeholders’ priorities and preferences for existing and potential solutions to air pollution with citizens’ lived experiences, thus assessing the political/technical feasibility and public acceptability of current and proposed measures. Furthermore, we will complement the primary qualitative data with a critical review examining the successes and failures of UK and European policies to draw lessons that can be relevant for Delhi and to avoid ineffective policies and achieve cost-effective solutions for the city in the shortest possible time.

In the present study, personal exposure to fine particulate matter (particulate matter with an aerodynamic diameter 2.5]) concentrations in an urban hotspot (central business district [CBD]) was investigated. The PM monitoring campaigns were carried out at an urban hotspot from June to October 2015. The personal exposure monitoring was performed during three different time periods, i.e., morning (8 a.m.−9 a.m.), afternoon (12.30 p.m.–1.30 p.m.), and evening (4 p.m.–5 p.m.), to cover both the peak and lean hour activities of the CBD. The median PM2.5 concentrations were 38.1, 34.9, and 40.4 µg/m3 during the morning, afternoon, and evening hours on the weekends. During weekdays, the median PM2.5 concentrations were 59.5, 29.6, and 36.6 µg/m3 in the morning, afternoon, and evening hours, respectively. It was observed that the combined effect of traffic emissions, complex land use, and micrometeorological conditions created localized air pollution hotspots. Furthermore, the total PM2.5 lung dose levels for an exposure duration of 1 hr were 8.7 ± 5.7 and 12.3 ± 5.2 µg at CBD during weekends and weekdays, respectively, as compared with 2.5 ± 0.8 µg at the urban background (UB). This study emphasizes the need for mobile measurement for short-term personal exposure assessment complementing the fixed air quality monitoring. Implications: Personal exposure monitoring at an urban hotspot indicated space and time variation in PM concentrations that is not captured by the fixed air quality monitoring networks. The short-term exposure to higher concentrations can have a significant impact on health that need to be considered for the health risk–based air quality management. The study emphasizes the need of hotspot-based monitoring complementing the already existing fixed air quality monitoring in urban areas. The personal exposure patterns at hotspots can provide additional insight into sustainable urban planning.