Delhi, India, routinely experiences some of the world's highest urban particulate matter concentrations. We established the Delhi Aerosol Supersite study to provide long-term characterization of the ...ambient submicron aerosol composition in Delhi. Here we report on 1.25 years of highly time-resolved speciated submicron particulate matter (PM.sub.1) data, including black carbon (BC) and nonrefractory PM.sub.1 (NR-PM.sub.1 ), which we combine to develop a composition-based estimate of PM.sub.1 ("C-PM.sub.1 " = BC + NR-PM.sub.1) concentrations.
Low-cost sensors (LCS) offer the opportunity to measure urban air quality at a spatiotemporal scale that is finer than what is currently practical with expensive research- or regulatory-grade ...instruments. Recently, the LCS research community has focused largely on sensor calibration, pollution monitoring, and exposure assessment; here, we investigate the applicability of LCS for characterizing particulate pollution sources in an urban environment. Using an integrated multipollutant LCS system (which measures both gases and particles), we collected air quality data for 6 weeks during the winter at a site in Delhi, India. The results were compared to measurements taken by co-located research-grade particle instruments. Non-negative matrix factorization was used to deconvolve LCS data into unique factors that were then identified by examining the factor composition and comparing them to the research-grade measurements. The data were described well by three factors: a combustion factor characterized by high CO levels and two factors characterized by measured particles. These factors align well with measurements by research-grade instruments, including particle types determined from factor analysis of online particle composition measurements. This work demonstrates that multipollutant LCS measurements, despite their inherent limitations (e.g., calibration challenges and inability to measure smallest particles), can provide insight into sources of fine particulate matter in a complex urban environment.
PM2.5 pollution in Delhi averaged 150 μg/m3 from 2012 through 2014, which is 15 times higher than the World Health Organization's annual-average guideline. For this setting, we present on-road ...exposure of PM2.5 concentrations for 11 transport microenvironments along a fixed 8.3-km arterial route, during morning rush hour. The data collection was carried out using a portable TSI DustTrak DRX 8433 aerosol monitor, between January and May (2014). The monthly-average measured ambient concentrations varied from 130 μg/m3 to 250 μg/m3. The on-road PM2.5 concentrations exceeded the ambient measurements by an average of 40% for walking, 10% for cycle, 30% for motorised two wheeler (2W), 30% for open-windowed (OW) car, 30% for auto rickshaw, 20% for air-conditioned as well as for OW bus, 20% for bus stop, and 30% for underground metro station. On the other hand, concentrations were lower by 50% inside air-conditioned (AC) car and 20% inside the metro rail carriage. We find that the percent exceedance for open modes (cycle, auto rickshaw, 2W, OW car, and OW bus) reduces non-linearly with increasing ambient concentration. The reduction is steeper at concentrations lower than 150 μg/m3 than at higher concentrations. After accounting for air inhalation rate and speed of travel, PM2.5 mass uptake per kilometer during cycling is 9 times of AC car, the mode with the lowest exposure. At current level of concentrations, an hour of cycling in Delhi during morning rush-hour period results in PM2.5 dose which is 40% higher than an entire-day dose in cities like Tokyo, London, and New York, where ambient concentrations range from 10 to 20 μg/m3.
•Measurements of on-road PM2.5 exposures in 11 transport microenvironments in Delhi.•Traveling in auto rickshaw leads to 30% higher exposure rate than in an off-road location.•Inside air-conditioned cars and metro carriages, the exposure rate is the lowest.•PM2.5 mass inhaled per km is 9 times for cycling compared to inside of an AC car.
Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) ...owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.
Delhi is a megacity subject to high local anthropogenic emissions and long-range transport of pollutants. This work presents for the first time time-resolved estimates of hygroscopicity parameter (κ) ...and cloud condensation nuclei (CCN), spanning for more than a year, derived from chemical composition and size distribution data. As a part of the Delhi Aerosol Supersite (DAS) campaign, the characterization of aerosol composition and size distribution was conducted from January 2017 to March 2018. Air masses originating from the Arabian Sea (AS), Bay of Bengal (BB), and southern Asia (SA) exhibited distinct characteristics of time-resolved sub-micron non-refractory PM1 (NRPM1) species, size distributions, and CCN number concentrations. The SA air mass had the highest NRPM1 loading with high chloride and organics, followed by the BB air mass, which was more contaminated than AS, with a higher organic fraction and nitrate. The primary sources were identified as biomass-burning, thermal power plant emissions, industrial emissions, and vehicular emissions. The average hygroscopicity parameter (κ), calculated by the mixing rule, was approximately 0.3 (varying between 0.13 and 0.77) for all the air masses (0.32±0.06 for AS, 0.31±0.06 for BB, and 0.32±0.10 for SA). The diurnal variations in κ were impacted by the chemical properties and thus source activities. The total, Aitken, and accumulation mode number concentrations were higher for SA, followed by BB and AS. The mean values of estimated CCN number concentration (NCCN; 3669–28926 cm−3) and the activated fraction (af; 0.19–0.87), for supersaturations varying from 0.1 % to 0.8 %, also showed the same trend, implying that these were highest in SA, followed by those in BB and then those in AS. The size turned out to be more important than chemical composition directly, and the NCCN was governed by either the Aitken or accumulation modes, depending upon the supersaturation (SS) and critical diameter (Dc). af was governed mainly by the geometric mean diameter (GMD), and such a high af (0.71±0.14 for the most dominant sub-branch of the SA air mass – R1 – at 0.4 % SS) has not been seen anywhere in the world for a continental site. The high af was a consequence of very low Dc (25–130 nm, for SS ranging from 0.1 % to 0.8 %) observed for Delhi. Indirectly, the chemical properties also impacted CCN and af by impacting the diurnal patterns of Aitken and accumulation modes, κ and Dc. The high-hygroscopic nature of aerosols, high NCCN, and high af can severely impact the precipitation patterns of the Indian monsoon in Delhi, impact the radiation budget, and have indirect effects and need to be investigated to quantify this impact.
Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data ...requirements for mapping a city’s air quality using mobile monitors with “data-only” versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a “data-only” approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R 2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1–2) repeated drives but obtained better cross-validation R 2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.
Delhi, India, is the second most populated city in the world and routinely experiences some of the highest particulate matter concentrations of any megacity on the planet, posing acute challenges to ...public health (World Health Organization, 2018). However, the current understanding of the sources and dynamics of PM pollution in Delhi is limited. Measurements at the Delhi Aerosol Supersite (DAS) provide long-term chemical characterization of ambient submicron aerosol in Delhi, with near-continuous online measurements of aerosol composition. Here we report on source apportionment based on positive matrix factorization (PMF), conducted on 15 months of highly time-resolved speciated submicron non-refractory PM1 (NR-PM1) between January 2017 and March 2018. We report on seasonal variability across four seasons of 2017 and interannual variability using data from the two winters and springs of 2017 and 2018. We show that a modified tracer-based organic component analysis provides an opportunity for a real-time source apportionment approach for organics in Delhi. Phase equilibrium modeling of aerosols using the extended aerosol inorganics model (E-AIM) predicts equilibrium gas-phase concentrations and allows evaluation of the importance of the ventilation coefficient (VC) and temperature in controlling primary and secondary organic aerosol. We also find that primary aerosol dominates severe air pollution episodes, and secondary aerosol dominates seasonal averages.
The Indian national capital, Delhi, routinely experiences some of the world's highest urban particulate matter concentrations. While fine particulate matter (PM2.5) mass concentrations in Delhi are ...at least an order of magnitude higher than in many western cities, the particle number (PN) concentrations are not similarly elevated. Here we report on 1.25 years of highly time-resolved particle size distribution (PSD) data in the size range of 12–560 nm. We observed that the large number of accumulation mode particles – that constitute most of the PM2.5 mass – also contributed substantially to the PN concentrations. The ultrafine particle (UFP; Dp<100 nm) fraction of PNs was higher during the traffic rush hours and for daytimes of warmer seasons, which is consistent with traffic and nucleation events being major sources of urban UFPs. UFP concentrations were found to be relatively lower during periods with some of the highest mass concentrations. Calculations based on measured PSDs and coagulation theory suggest UFP concentrations are suppressed by a rapid coagulation sink during polluted periods when large concentrations of particles in the accumulation mode result in high surface area concentrations. A smaller accumulation mode for warmer months results in an increased UFP fraction, likely owing to a comparatively smaller coagulation sink. We also see evidence suggestive of nucleation which may also contribute to the increased UFP proportions during the warmer seasons. Even though coagulation does not affect mass concentrations, it can significantly govern PN levels with important health and policy implications. Implications of a strong accumulation mode coagulation sink for future air quality control efforts in Delhi are that a reduction in mass concentration, especially in winter, may not produce a proportional reduction in PN concentrations. Strategies that only target accumulation mode particles (which constitute much of the fine PM2.5 mass) may even lead to an increase in the UFP concentrations as the coagulation sink decreases.
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