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We investigated the influence of biomass burning (BURN), Diwali fireworks, and fog events on the ambient fine particulate matter (PM

A major fraction of organic aerosol (OA) is water-soluble. The water-soluble organic aerosol (WSOA) significantly impacts aerosol hygroscopicity and cloud condensation nuclei (CCN) formation, and adversely affects human health. In this study, characterization of WSOA in PM2.5 was done for the samples collected at two sites (Hauz Khas and Pusa) in Delhi NCR, one of the most polluted cities in the world, during the agricultural crop-residue burning period (October-November, 2019) and winter (December, 2019) using offline aerosol mass spectrometry (AMS) technique and compared with co-located real-time AMS measurements. Offline AMS provides quantitative separation of OA factors that can be primary or secondary. Offline AMS analysis showed that approximately 68% and 64% of OA are water-soluble in Hauz Khas and Pusa, respectively, which was corroborated by the filter-based measurements of water-soluble organic carbon (WSOC) and organic carbon (OC) using a TOC-L analyzer and OCEC analyzer, respectively. Three primary factors, including traffic, biomass burning, and solid fuel combustion, and two secondary factors (or sources) were resolved with Positive Matrix Factorization (PMF) analysis on the WSOA data from offline AMS. The results showed that secondary factors dominated the WSOA (~41%), followed by biomass-burning organic aerosol (BBOA) (30-34%). In addition, the recoveries of the organic factors from several sources, including traffic, biomass burning, solid fuel combustion, and secondary organic aerosol are discussed. More oxidized organic aerosol (MO-OOA) is highly water soluble (88-92%), representing highly oxidized compounds generated from aqueous-phase reactions. The relatively small contribution of hydrocarbon-like organic aerosol (HOA) to WSOA was most likely due to their low water solubility. Overall, this study improves the understanding of the OA sources and their water solubility over the study region.

Ultrafine particles (UFP), i.e. particles with an aerodynamic diameter below 100 nm, have a significant impact on public health and the hydrological cycle. Recent studies showed that their ability to penetrate more deeply into the lungs and potentially into the blood stream may cause an increased incidence of cardiovascular and cerebrovascular diseases. Additionally, UFPs significantly contribute to cloud condensation nuclei concentrations. However, knowledge on global distributions of UFPs is scarce.We present a global simulation of UFP concentrations using the ECHAM/MESSy Atmospheric Chemistry Model (EMAC), including tropospheric and middle-atmospheric processes, and the modal aerosol microphysics submodel GMXe. Due to the high sensitivity of UFP concentrations to the size distribution of emitted particles, we derived emission median diameter for primary emissions from various sectors and species based on existing literature. We show the importance of primary emissions and nucleation on UFP concentrations as well as their composition, seasonality and vertical distributions.Model results were evaluated over Europe, the United States, India and China, using particle size distribution and particle number concentration measurements from available datasets and the literature. We obtain reasonable agreement between the model results and observations. However, the highest values of observed, street-level UFP concentrations are systematically underestimated, whereas in rural environments close to urban areas they are generally overestimated by the model. As the relatively coarse global model does not resolve concentration gradients in urban centres and local UFP hotspots, high-resolution data of anthropogenic emissions is used to account for such differences in each model grid box. This downscaling further improves the agreement with observations, decreasing the root mean squared logarithmic error and removing discrepancies associated with air quality and population density gradients within the model grid boxes.

A new global dataset of annual averaged ultrafine particle (UFP) concentrations at the Earth's surface has been developed through numerical simulations using the ECHAM/MESSy Atmospheric Chemistry model (EMAC). Size distributions of emitted particles from the contributing source sectors have been derived based on literature reports. The model results of UFP concentrations are evaluated using particle size distribution and particle number concentration measurements from available datasets and the literature. While we obtain reasonable agreement between the model results and observations (logarithmic scale correlation of r = 0.76 for non-remote, polluted regions), the highest values of observed, street-level UFP concentrations are systematically underestimated, whereas in rural environments close to urban areas the model generally overestimates observed UFP concentrations. As the relatively coarse global model does not resolve concentration gradients in urban centres and industrial UFP hotspots, high-resolution data of anthropogenic emissions is used to account for such differences in each model grid box, obtaining UFP concentrations with unprecedented 0.1° x 0.1° horizontal resolution at the Earth's surface. This observation-guided downscaling further improves the agreement with observations, leading to an increase of the logarithmic scale correlation between observed and simulated UFP concentrations to r = 0.84 in polluted environments (and 0.95 in all regions), a decrease of the root mean squared logarithmic error (from 0.57 to 0.43), and removes discrepancies associated with air quality and population density gradients within the model grid boxes. Model results are made publicly available for studies on public health and other impacts of atmospheric UFPs, and for intercomparison with other regional and global models and datasets. Under review for Atmospheric Chemistry and Physics (ACP)

Mitigation of carbonaceous aerosol emissions is expected to provide climate and health co-benefits. The accurate representation of carbonaceous aerosols in climate models is critical for reducing uncertainties in their climate feedback. In this regard, emission fluxes and aerosol life cycle processes are the two primary sources of uncertainties. Here, we demonstrate that the incorporation of a dynamic ageing scheme and emission estimates that are updated for the local sources improves the representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model, RegCM, compared with its default configuration. The respective mean black carbon (BC) and organic carbon (OC) surface concentrations in 2010 are estimated to be 4.25 and 10.35 µg m−3 over the Indo-Gangetic Plain (IGP) in the augmented model. The BC column burden over the polluted IGP is found to be 2.47 mg m−2, 69.95 % higher than in the default model configuration and much closer to available observations. The anthropogenic aerosol optical depth (AOD) increases by more than 19 % over the IGP due to the model enhancement, also leading to a better agreement with observed AOD. The respective top-of-the-atmosphere, surface, and atmospheric anthropogenic aerosol short-wave radiative forcing are estimated at −0.3, −9.3, and 9.0 W m−2 over the IGP and −0.89, −5.33, and 4.44 W m−2 over Peninsular India (PI). Our results suggest that the combined effect of two modifications leads to maximum improvements in the model performance in regions where emissions play a dominant role.

Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.

Rising awareness of the health risks posed by elevated levels of ground-level O3 and NO2 have led to an increased demand for affordable and dense spatio-temporal air quality monitoring networks. Low-cost sensors used as a part of Internet of Things (IoT) platforms offer an attractive solution with greater mobility and lower maintenance costs, and can supplement compliance regulatory monitoring stations. These commodity low-cost sensors have reasonably high accuracy but require in-field calibration to improve precision. In this paper, we report the results of a deployment and calibration study on a network of seven air quality monitoring devices built using the Alphasense O3 (OX-B431) and NO2 (NO2-B43F) electrochemical gas sensors. The sensors were deployed at sites situated within two mega-cities with diverse geographical, meteorological and air quality parameters – Faridabad (Delhi National Capital Region) and Mumbai, India. The deployment was done in two phases over a period of three months. A unique feature of our deployment is a swap-out experiment wherein four of these sensors were relocated to different sites in the two deployment phases. Such a diverse deployment with sensors switching places gives us a unique opportunity to ablate the effect of seasonal, as well as geographical variations on calibration performance. We perform an extensive study of more than a dozen parametric as well as non-parametric calibration algorithms and find local calibration methods to offer the best performance. We propose a novel local calibration algorithm based on metric-learning that offers, across deployment sites and phases, an average R2 coefficient of 0.873 with respect to reference values for O3 calibration and 0.886 for NO2 calibration. This represents an upto 9 % increase over R2 values offered by classical local calibration methods. In particular, our proposed model far outperforms the default calibration models offered by the gas sensor manufacturer. We also offer a critical analysis of the effect of various data preparation and model design choices on calibration performance. The key recommendations emerging out of this study include (1) incorporating ambient relative humidity and temperature as free parameters (or features) into all calibration models, (2) assessing the relative importance of various features with respect to the calibration task at hand, by using an appropriate feature weighing or metric learning technique, (3) the use of local (or even hyper-local) calibration techniques such as k-NN that seem to offer the best performance in high variability conditions such as those encountered in field deployments, (4) performing temporal smoothing over raw time series data, say by averaging sensor signals over small windows, but being careful to not do so too aggressively, and (5) making all efforts at ensuring that data with enough diversity is demonstrated to the calibration algorithm while training to ensure good generalization. These results offer insights into the strengths and limitations of these sensors, and offer an encouraging opportunity at using them to supplement and densify compliance regulatory monitoring networks.

A year-long offline based study was performed at five sites of Indo-Gangetic plain to get the spatio-temporal variation in elemental carbon (EC), organic carbon (OC), water-soluble organic carbon (WSOC) and secondary organic aerosol (SOA) in PM2.5 fraction. OC constitutes ~70–90% of the total carbon with high water-soluble fraction (60–70%) during winter, except at Hisar. The change in concentration of OC was insignificant during summer, whereas the water-soluble fraction increased by 75–85%. The relatively high OC/EC ratio (7–11) and OC/K+ ratio (10–52) during winter and post-monsoon suggest the dominance of biomass burning from wood fuel and agricultural waste. EC is nearly constant over the seasons at all sites suggesting the dominant presence of traffic-originated EC throughout the year. Interestingly, a low correlation (~0.3) between OC and EC at IITD and IITMD indicates their source from different origins. Exceptionally, the daytime OC/EC ratio was slightly higher than night time during winter at Hisar. However, other sites showed the opposite trend. A significant linear relationship between WSOC and secondary organic carbon suggests dominant contribution from SOA. A paired t-test results indicate much of WSOC is of secondary origin and regional in nature. The secondary fraction contributed 30–60% of OC during winter across all the sites in IGP, whereas during summer it varied between 20 and 40%. SOA estimated by the EC- tracer method has the highest contribution at IITMD (~138 μg/m3) among all the sites and peaked during post-monsoon.

The oxidative potential (OP) of ambient particulate matter (PM) is a metric commonly used to link the aerosol exposure to its adverse health effects. In this study, we report the first-ever real-ti...

While high concentrations of pre-existing particles tend to inhibit new particle formation (NPF) in the atmosphere, severely polluted megacities around the world are becoming hot spots for the latter. We measured the particle number-size distributions with a Scanning Mobility Particle Sizer (SMPS) in the urban environment of Kanpur, India, and discovered that particle bursts occurred on 82% of the observation days, indicating that new particles frequently formed from gaseous precursors despite the relatively high concentrations of pre-existing particles. During such events, Aitken-mode particles contributed more than 50% of the total particle mass. Additionally, we used a high-resolution time-of-flight aerosol mass spectrometer (HR-ToF-AMS) to assess chemical changes in the sub-micron particles during NPF events. Because the HR-ToF-AMS can not detect particles smaller than 40 nm in diameter, however, it was not possible to investigate the chemistry driving the NPF. Our results indicated that oxygenated organic aerosols (OAs) constituted almost 77%—the largest fraction—of the sub-micron particles. The m/z 57 ion (C4H9+), a tracer of hydrocarbon-like OA (HOA), displayed significantly enhanced signal intensity during all of the NPF event days. Moreover, the increased proportion of organic ions, m/z 44 (CO2+), on these days suggested the presence of less volatile, highly oxidized OAs (LV-OOAs), revealing that the growth of new particles was mainly due to the condensation of low-volatility organic species. The substantially elevated signal intensity of amines (viz., CHN+, CH4N+, C2H4N+, C3H8N+, and C5H12N+) in the sub-micron aerosols during NPF further demonstrated that these nitrogen-containing organic compounds may have played a critical role in these events. Thus, our findings emphasize the relevance of amines to secondary aerosol formation in severely polluted urban environments.

Mitigation of carbonaceous aerosol emissions is expected to provide climate and health co-benefits. The accurate representation of carbonaceous aerosols in climate models is critical for reducing uncertainties in their climate feedbacks. In this regard, emission fluxes and aerosol life-cycle processes are the two primary sources of uncertainties. Here we demonstrate that incorporating a dynamic ageing scheme and emission estimates that are updated for the local sources improve the representation of carbonaceous aerosols over the Indian monsoon region in a regional climate model, RegCM, compared to its default configuration. The mean BC and OC surface concentrations in 2010 are estimated to be 4.25 and 10.35 μg m−3, respectively, over the Indo-Gangetic Plain (IGP), in the augmented model. The BC column burden over the polluted IGP is found to be 2.47 mg m−2, 69.95 % higher than in the default model configuration and much closer to available observations. The anthropogenic AOD increases by more than 19 % over the IGP due to the model enhancement, also leading to a better agreement with observed AOD. The top-of-the-atmosphere, surface, and atmospheric anthropogenic aerosol shortwave radiative forcing are estimated at −0.3, −9.3, and 9.0 W m−2, respectively, over the IGP and −0.89, −5.33, and 4.44 W m−2, respectively, over Peninsular India. Our results suggest that both the accurate estimates of emission fluxes and a better representation of aerosol processes are required to improve the aerosol life cycle representation in the climate model.

In recent times, a significant number of studies on the composition and sources of fine particulate matters and volatile organic compounds have been carried out over Delhi, either initiated by or in association with the researchers from the Indian Institute of Technology Kanpur (IIT Kanpur), in collaboration with researchers from within and outside India. All these studies utilized highly time-resolved, campaign-mode observations made with state-of-the-art instrumentation during the late winter months (mid-January to March) of 2018. Individually, each of these studies were rigorous in nature, containing explicit detailing about different types of ambient air pollutants in Delhi such as organic aerosols, inorganic elements, metals, carbonaceous aerosols, and volatile organic compounds. This study consolidates the extremely useful knowledge on source attribution of these air pollutants in the Delhi National Capital Region currently contained in these fragmented studies, which is vital to further enhancing our understanding of composition, characteristics, and sources of air pollutants over Delhi, as well as to designing appropriate mitigation measures tailored to local specifics.

Characterizing the chemical composition of ambient particulate matter (PM) provides valuable information on the concentration of secondary species and toxic metals and assists in the validation of abatement techniques. The chemical components of PM can be measured by sampling on filters and analyzing them in the laboratory or using real-time measurements of the species. It is important for the accuracy of the PM monitoring networks that measurements from the offline and online methods are comparable and biases are known. The concentrations of water-soluble inorganic ions (NO3−, SO42−, NH4+, and Cl−) in PM2.5 measured from 24 h filter samples using ion chromatography (IC) were compared with the online measurements of inorganics from an aerosol mass spectrometer (AMS) with a frequency of 2 min. Also, the concentrations of heavy and trace elements determined from 24 h filter samples using inductively coupled plasma mass spectroscopy (ICP-MS) were compared with the online measurements of half-hourly heavy and trace metal concentrations from an Xact 625i ambient metal mass monitor. The comparison was performed over two seasons (summer and winter) and at two sites (Indian Institute of Technology Delhi (IITD) and Indian Institute of Tropical Meteorology, Delhi (IITMD)) which are located in the Delhi National Capital Region (NCR), India, one of the most heavily polluted urban areas in the world. Collocated deployments of the instruments helped to quantify the differences between online and offline measurements and evaluate the possible reasons for positive and negative biases. The slopes for SO42− and NH4+ were closer to the 1:1 line during winter and decreased during summer at both sites. The higher concentrations on the filters were due to the formation of particulate (NH4)2SO4. Filter-based NO3− measurements were lower than online NO3− during summer at IITD and winter at IITMD due to the volatile nature of NO3− from the filter substrate. Offline-measured Cl− was consistently higher than AMS-derived Cl− during summer and winter at both sites. Based on their comparability characteristics, elements were grouped into three categories. The online element data were highly correlated (R2>0.8) with the offline measurements for Al, K, Ca, Ti, Zn, Mn, Fe, Ba, and Pb during summer at IITD and winter at both the sites. The higher correlation coefficient demonstrated the precision of the measurements of these elements by both the Xact 625i and ICP-MS. Some of these elements showed higher Xact 625i elemental concentrations than ICP-MS measurements by an average of 10 %–40 % depending on the season and site. The reasons for the differences in the concentration of the elements could be the distance between two inlets for the two methods, line interference between two elements in Xact measurements, the sampling strategy, variable concentrations of elements in blank filters, and the digestion protocol for ICP-MS measurements.

Urban sanitation infrastructure is inadequate in many low-income countries, leading to the presence of highly concentrated, uncontained fecal waste streams in densely populated areas. Combined with mechanisms of aerosolization, airborne transport of enteric microbes and their genetic material is possible in such settings but remains poorly characterized. We detected and quantified enteric pathogen-associated gene targets in aerosol samples near open wastewater canals (OWCs) or impacted (receiving sewage or wastewater) surface waters and control sites in La Paz, Bolivia; Kanpur, India; and Atlanta, USA, via multiplex reverse-transcription qPCR (37 targets) and ddPCR (13 targets). We detected a wide range of enteric targets, some not previously reported in extramural urban aerosols, with more frequent detections of all enteric targets at higher densities in La Paz and Kanpur near OWCs. We report density estimates ranging up to 4.7 × 102 gc per mair3 across all targets including heat-stable enterotoxigenic Escherichia coli, Campylobacter jejuni, enteroinvasive E. coli/Shigella spp., Salmonella spp., norovirus, and Cryptosporidium spp. Estimated 25, 76, and 0% of samples containing positive pathogen detects were accompanied by culturable E. coli in La Paz, Kanpur, and Atlanta, respectively, suggesting potential for viability of enteric microbes at the point of sampling. Airborne transmission of enteric pathogens merits further investigation in cities with poor sanitation.

Air pollution is a severe problem growing over time. A dense air-quality monitoring network is needed to update the people regarding the air pollution status in cities. A low-cost sensor device (LCSD) based dense air-quality monitoring network is more viable than continuous ambient air quality monitoring stations (CAAQMS). An in-field calibration approach is needed to improve agreements of the LCSDs to CAAQMS. The present work aims to propose a calibration method for PM2.5 using domain adaptation technique to reduce the collocation duration of LCSDs and CAAQMS. A novel calibration approach is proposed in this work for the measured PM2.5 levels of LCSDs. The dataset used for the experimentation consists of PM2.5 values and other parameters (PM10, temperature, and humidity) at hourly duration over a period of three months data. We propose new features, by combining PM2.5, PM10, temperature, and humidity, that significantly improved the performance of calibration. Further, the calibration model is adapted to the target location for a new LCSD with a collocation time of two days. The proposed model shows high correlation coefficient values (R2) and significantly low mean absolute percentage error (MAPE) than that of other baseline models. Thus, the proposed model helps in reducing the collocation time while maintaining high calibration performance.

BackgroundSoots are known to cause many diseases in humans, but their underlying mechanisms of toxicity are still not known. Here, we report that soots induce cell proliferation of lung epithelial cells via modulating autophagy pathways.ResultsFullerene soot and diesel exhaust particles (DEP) induced cell proliferation of lung epithelial, A549 cells via distinct autophagic mechanisms and did not cause cell death. Exposure of fullerene soot protected the cell death of A549 cells, caused by hydrogen peroxide, and inhibited LPS-induced autophagy. Fullerene soot co-localized with the autophagic proteins and inhibited starvation-induced autophagy (downregulated ATG-5, beclin-1, p62, and LC3 expressions) independent of its antioxidant properties. Similarly, it decreased the expression profile of autophagic genes and upregulated the proliferation-responsive gene, Ki-67, in mice. We observed that expressions of fullerene soot-responsive genes (Beclin-1, ATG-5, and p62) were reverted by Akt Inhibitor X, indicating an important role of the Akt pathway. At an elemental level, we found that elemental carbon of fullerene soot may be converted into organic carbon, as measured by OCEC, which may point fullerene soot as a source of carbon. On the other hand, DEP upregulated the expressions of autophagy genes. Akt Inhibitor X did not attenuate DEP-induced cell proliferation and autophagic response. However, an autophagic inhibitor, chloroquine, and significantly inhibited DEP-induced cell proliferation.ConclusionIt can be said that distinct autophagic mechanisms are operational in cell proliferation of lung epithelial cells due to soots, which may be responsible for different diseases. Understanding the mechanism of these pathways provides some important targets, which can be utilized for the development of future therapeutics.

In the event of a nuclear reactor accident, large amount of radioactivity in the form of fission products may get released to the piping assembly of primary heat transport system. These fission products mostly in the form of aerosol particles get deposited on the inner surface of the piping system due to various depositional processes. The removal processes in the complex piping system are controlled to a large extent by the thermal-hydraulic conditions like temperature, pressure and flow rates. These parameters generally vary with time and therefore must be carefully monitored to predict the aerosol behavior in the piping system. Experimental determination of the deposition fraction, interpretation of the role of controlling parameters and development/validation of theoretical models are key areas gaining constant attention among researchers. In the present work, experiments were conducted in a piping assembly consisting of bends and horizontal-vertical orientation at two different carrier gas flow rates. Deposition fractions in the test assembly were estimated and the role of different dynamical processes (thermophoresis, gravitation and bend impaction) was interpreted. The computational fluid dynamics modeling approach is used for theoretical simulation in this work. Aerosol behavior in terms of number concentration, particle size distribution, and particle deposition in the piping system was simulated with the computational fluid dynamics software ANSYS Fluent 16.0 and the results were compared with experimental measurements (wherever applicable).

Secondary organic aerosol (SOA) is a significant component of organic aerosol (OA), formed majorly via aqueous-phase processing (aqSOA) during winter fog period yet it persist as a significant source of uncertainties within climate models due to lack of sufficient knowledge about its ambient formation and evolution processes. Kanpur is situated at the center of Indo-Gangetic Plain which witnesses several fog episodes every year during winter time. In this study, we have evaluated the effect of aqueous-phase processing on the formation pathway of OA, its composition and oxidative properties during five fog processing periods, i.e., Pre-Fog-Period (Pr-F-P), Activating-Fog-Period (A-F-P), Fog-Period (F-P), Dissipating-Fog-Period (D-F-P), and Post-Fog-Period (Po-F-P). A-F-P was observed as heavily polluted period (249.8 ± 47.8 μg/m3) while F-P period was least polluted (153.1 ± 37.8 μg/m3) indicating the wet removal through grown fog droplets. Oxygenated organic aerosol (OOA-1) processed mainly through biomass burning (BB) emission suggesting is a good surrogate of aqueous SOA, and it gets enhanced significantly during high biomass-burning emissions during A-F-P and D-F-P under acidic aerosol conditions. Also, in contrast to formation mechanism, our results proposing specific formation process (fragmentation, functionalization, or oligomerization) for different fog processing periods. A-F-P and D-F-P periods are crucial possibly for the formation of OA dominant through oligomerization mechanism in which functionalization of OH moieties occurs in A-F-P periods, whereas oligomerization mechanism with the addition of carbonyl (aldehyde/ketone) moieties could occur in D-F-P periods, accompanied by acidic aerosol as well as high aerosol liquid water content (ALWC). In contrast, during OA evolution process, Van Krevelen (VK) slope, O/C ratio, SOA O/C ratio (O/COOA), average oxidation state of carbon for bulk-aerosol (OSC) and SOA (OSC)OOA, substantially varies throughout all the fog processing periods.

Studies of behavioral characteristics of fission product aerosols are extremely important in context of suspected environmental release during a severe nuclear reactor accident. Fission product aerosols generated in such a case are expected to travel from coolant systems to the containment and may get released to the environment in the most unlikely scenario of containment breach. Presence of steam also affects their dynamic behaviour and fate. Interaction of aerosol particles with water vapor in subsaturation domain modifies their physical and chemical characteristics. The objective of this study was to examine the hygroscopic nature of nuclear accident relevant cesium bound compounds viz. CsI, CsOH with the use of Hygroscopic Tandem Differential Mobility Analyzer (HTDMA). The growth factors for single component aerosols were obtained for different initial size of particles at different relative humidity levels. Growth factor curves as well as deliquescent parameters for CsI and CsOH particles have been obtained for the first time and have a crucial role towards environmental source term estimations in the event of nuclear accident. Experimental results were also compared with the theories available in literature.

This work highlights the first multiweek size-resolved analysis of high-resolution (HR) mass spectra from the Aerodyne aerosol mass spectrometer (AMS). High-resolution analysis allows for separatio...

Diwali is among the most important Indian festivals, and elaborate firework displays mark the evening's festivities. This study assesses the impact of Diwali on the concentration, composition, and sources of ambient PM2.5. We observed the total PM2.5 concentrations to rise to 16 times the pre-firework levels, while each of the elemental, organic, and black carbon fractions of ambient PM2.5 increased by a factor of 46.1, 3.7, and 5.6, respectively. The concentration of species like K, Al, Sr, Ba, S, and Bi displayed distinct peaks during the firework event and were identified as tracers. The average concentrations of potential carcinogens, like As, exceeded US EPA screening levels for industrial air by a factor of ~9.6, while peak levels reached up to 16.1 times the screening levels. The source apportionment study, undertaken using positive matrix factorization, revealed the fireworks to account for 95% of the total elemental PM2.5 during Diwali. The resolved primary organic emissions, too, were enhanced by a factor of 8 during Diwali. Delhi has encountered serious haze events following Diwali in recent years; this study highlights that biomass burning emissions rather than the fireworks drive the poor air quality in the days following Diwali., Comment: The manuscript is accepted for publication in Atmospheric Pollution Research. Present Status: Accepted

Aerosols emitted in densely populated and industrialized Indo‐Gangetic Plain, one of the most polluted regions in the world, modulate regional climate, monsoon, and Himalayan glacier retreat. Thus, this region is important for understanding aerosol perturbations and their resulting impacts on atmospheric changes during COVID‐19 lockdown period, a natural experimental condition created by the pandemic. By analyzing 5 years (2016–2020) data of aerosols and performing a radiative transfer calculation, we found that columnar and near‐surface aerosol loadings decreased, leading to reductions in radiative cooling at the surface and top of the atmosphere and atmospheric warming during lockdown period. Further, satellite data analyses showed increases in cloud optical thickness and cloud‐particle effective radius and decrease in lower tropospheric air temperature during lockdown period. These results indicate critical influences of COVID‐19 lockdown on regional climate and water cycle over Indo‐Gangetic Plain, emphasizing need for further studies from modeling perspectives., Key Points Aerosols reduced by COVID‐19 lockdown perturbed atmospheric heat budget over the Indo‐Gangetic PlainCloud optical thickness and cloud‐particle effective radius increased during COVID‐19 lockdown periodAir temperature in the lower troposphere decreased during COVID‐19 lockdown period

Cesium compounds if present in atmosphere, can affect human health as well as the ecosystem due to their highly hazardous nature. Interaction of cesium compounds with abundantly available atmospheric salts can modify the hygroscopic behavior in sub-saturation relative humidity (RH) domain. Any marked modification in growth factor (GF) for the mixed particle state in comparison to the single particles ultimately affects the settling rates and hence the deposition flux. This work studies the hygroscopic behavior of two important cesium bound fission product aerosols (CsI, CsOH) internally mixed with some common atmospheric particles viz. N a C l , ( N H 4 ) 2 S O 4 and NaNO3 for a fixed dry particle size of 100 nm. Experimental measurements, performed with Hygroscopic tandem differential mobility analyzer in the range of 20–94% RH, have been compared with the predictions made from Zdanovskii-Stokes-Robinson (ZSR) approach. Apart from the single/pure particle state for the constituents (i.e. mixing ratios 1:0 and 0:1), three other mixing ratios 1:4, 1:1 and 4:1 have been considered. The results show that the GF vs RH pattern for mixed particles is different from that for single CsI and CsOH particles. The intrinsic water uptake behavior for these cesium compounds was found to be perturbed for some of the chosen combinations as well. Deliquescent transition for the mixed particles was observed at lower RH compared to the single electrolytes. Relative differences noticeable for the chosen mixing ratios could be related to the available fractions in the mixed state. Overall, ZSR method was found to be capturing the trend of increasing GFs with increasing RH. Terminal gravitational settling velocities calculated from the measured GFs were also found to be different for single and mixed particles. The relative difference was significant for some combinations and test conditions. Any modification in settling velocity ultimately impacts the deposition flux estimations. Hence neglecting the presence of atmospheric salts affects the accuracy of the source term estimates for a postulated nuclear reactor accident scenario.

Urban sanitation infrastructure is inadequate in many low-income countries, leading to the presence of highly concentrated, uncontained fecal waste streams in densely populated areas. Combined with mechanisms of aerosolization, airborne transport of enteric microbes and their genetic material is possible in such settings but remains poorly characterized. We detected and quantified enteric pathogen-associated gene targets in aerosol samples near open wastewater canals (OWCs) or wastewater-impacted surface waters and control sites in La Paz, Bolivia; Kanpur, India; and Atlanta, USA via multiplex reverse-transcription qPCR (37 targets) and ddPCR (13 targets). We detected a wide range of enteric targets, some not previously reported in extramural urban aerosols, with more frequent detections of all enteric targets at higher densities in La Paz and Kanpur near OWCs. We report density estimates ranging up to 4.7 × 102 gc per m3air across all targets including heat stabile enterotoxigenic E. coli, C. jejuni, enteroinvasive E. coli/Shigella spp., Salmonella spp., norovirus, and Cryptosporidium spp. An estimated 25%, 76%, and 0% of samples containing positive pathogen detects were accompanied by culturable E. coli in La Paz, Kanpur, and Atlanta, respectively, suggesting potential for viability of enteric microbes at the point of sampling. Airborne transmission of enteric pathogens merits further investigation in cities with poor sanitation.SYNOPSISWe detected and quantified molecular targets associated with important enteric pathogens in outdoor aerosols in cities with poor sanitation to assess the potential role of the aeromicrobiological pathway in enteric infection transmission in such settings.

Availability of global satellite based Soil Moisture (SM) data has promoted the emergence of many applications in climate studies, agricultural water resource management and hydrology. In this context, validation of the global data set is of substance. Remote sensing measurements which are representative of an area covering 100 m2 to tens of km2 rarely match with in situ SM measurements at point scale due to scale difference. In this paper we present the new Indian Cosmic Ray Network (ICON) and compare it’s data with remotely sensed SM at different depths. ICON is the first network in India of the kind. It is operational since 2016 and consist of seven sites equipped with the COSMOS instrument. This instrument is based on the Cosmic Ray Neutron Probe (CRNP) technique which uses non-invasive neutron counts as a measure of soil moisture. It provides in situ measurements over an area with a radius of 150–250 m. This intermediate scale soil moisture is of interest for the validation of satellite SM. We compare the COSMOS derived soil moisture to surface soil moisture (SSM) and root zone soil moisture (RZSM) derived from SMOS, SMAP and GLDAS_Noah. The comparison with surface soil moisture products yield that the SMAP_L4_SSM showed best performance over all the sites with correlation (R) values ranging from 0.76 to 0.90. RZSM on the other hand from all products showed lesser performances. RZSM for GLDAS and SMAP_L4 products show that the results are better for the top layer R = 0.75 to 0.89 and 0.75 to 0.90 respectively than the deeper layers R = 0.26 to 0.92 and 0.6 to 0.8 respectively in all sites in India. The ICON network will be a useful tool for the calibration and validation activities for future SM missions like the NASA-ISRO Synthetic Aperture Radar (NISAR).

The Government of India (GOI) announced a nationwide lockdown starting 25th March 2020 to contain the spread of COVID-19, leading to an unprecedented decline in anthropogenic activities and in turn improvements in ambient air quality. This is the first study to focus on highly time-resolved chemical speciation and source apportionment of PM$_{2.5}$ to assess the impact of the lockdown and subsequent relaxations on the sources of ambient PM$_{2.5}$ in Delhi, India. The elemental, organic, and black carbon fractions of PM$_{2.5}$ were measured at the IIT Delhi campus from February 2020 to May 2020. We report source apportionment results using positive matrix factorization (PMF) of organic and elemental fractions of PM$_{2.5}$ during the different phases of the lockdown. The resolved sources such as vehicular emissions, domestic coal combustion, and semi-volatile oxygenated organic aerosol (SVOOA) were found to decrease by 96%, 95%, and 86%, respectively, during lockdown phase-1 as compared to pre-lockdown. An unforeseen rise in O$_3$ concentrations with declining NO$_x$ levels was observed, similar to other parts of the globe, leading to the low-volatility oxygenated organic aerosols (LVOOA) increasing to almost double the pre-lockdown concentrations during the last phase of the lockdown. The effect of the lockdown was found to be less pronounced on other resolved sources like secondary chloride, power plants, dust-related, hydrocarbon-like organic aerosols (HOA), and biomass burning related emissions, which were also swayed by the changing meteorological conditions during the four lockdown phases. The results presented in this study provide a basis for future emission control strategies, quantifying the extent to which constraining certain anthropogenic activities can ameliorate the ambient air., The article has been accepted for publication in Environment International ISSN: 0160-4120. Please note that the article below has not yet been subjected to formatting and copyediting in line with journal requirements, which may result in some changes prior to publication

In India, high rates of antibiotic consumption and poor sanitation infrastructure combine to pose a significant risk to the public through the environmental transmission of antimicrobial resistance (AMR). The WHO has declared extended-spectrum beta-lactamase (ESBL)-positive Escherichia coli a key indicator for the surveillance of AMR worldwide. In the current study, we measured the prevalence of AMR bacteria in an urban aquatic environment in India by detecting metabolically active ESBL-positive E. coli. Water samples were collected in duplicate from 16 representative environmental water sources including open canals, drains, and rivers around Kanpur, Uttar Pradesh. We detected culturable E. coli in environmental water at 11 (69%) of the sites. Out of the 11 sites that were positive for culturable E. coli, ESBL-producing E. coli was observed at 7 (64%). The prevalence of ESBL-producing E. coli detected in the urban aquatic environment suggests a threat of AMR bacteria to this region.

A nationwide lockdown was imposed in India due to COVID-19 pandemic in five phases from 25th March to May 31, 2020. The lockdown restricted major anthropogenic activities, primarily vehicular and industrial, thereby reducing the particulate matter concentration. This work investigates the variation in Black Carbon (BC) concentration and its sources (primarily Fossil Fuel (ff) burning and Biomass Burning (bb)) over Delhi from 18th February to July 31, 2020, covering one month of pre-lockdown phase, all the lockdown phases, and two months of successive lockdown relaxations. The daily average BC concentration varied from 0.22 to 16.92 μg/m3, with a mean value of 3.62 ± 2.93 μg/m3. During Pre-Lockdown (PL, 18th Feb-24th March 2020), Lockdown-1 (L1, 25th March-14th April 2020), Lockdown-2 (L2, 15th April-3rd May 2020), Lockdown-3 (L3, 4th-17th May 2020), Lockdown-4 (L4, 18th-31st May 2020), Unlock-1 (UN1, June 2020), and Unlock-2 (UN2, July 2020) the average BC concentrations were 7.93, 1.73, 2.59, 3.76, 3.26, 2.07, and 2.70 μg/m3, respectively. During the lockdown and unlock phases, BC decreased up to 78% compared to the PL period. The BC source apportionment studies show that fossil fuel burning was the dominant BC source during the entire sampling period. From L1 to UN2 an increasing trend in BCff contribution was observed (except L3) due to the successive relaxations given to anthropogenic activities. BCff contribution dipped briefly during L3 due to the intensive crop residue burning events in neighboring states. CWT analysis showed that local emission sources were the dominant contributors to BC concentration over Delhi., Graphical abstract Image 1

We report investigations of the structural, magnetic, electrical transport, and thermal properties of five compositions of the metallic perovskite ${\mathrm{Gd}\mathrm{Rh}}_{3}{\mathrm{C}}_{1\ensuremath{-}x}{\mathrm{B}}_{x}$ ($0.00\ensuremath{\le}x\ensuremath{\le}1.00$). Our results show that all five compositions undergo magnetic ordering at low temperatures, but the nature of the ordered state is significantly different in the carbon- and the boron-rich compositions, where the former shows signatures of an amplitude-modulated magnetic structure and the latter exhibits evidence of an equal-moment incommensurate antiferromagnetic ordering. We also observe a remarkable field-dependent evolution of conduction carrier polarization in the compositionally disordered compounds. The outcomes indicate that this system is energetically situated in proximity to a magnetic instability where small variations in the control parameter(s), such as the lattice constant and/or electron density, lead to considerably different ground states.

The past decade has seen episodes of increasingly severe air pollution across much of the highly populated Indo-Gangetic Plain (IGP), particularly during the post-monsoon season when crop residue burning (CRB) is most prevalent. Recent studies have suggested that a major, possibly dominant contributor to this air quality decline is that northwest (NW) Indian rice residue burning has shifted later into the post-monsoon season, as an unintended consequence of a 2009 groundwater preservation policy that delayed the sowing of irrigated rice paddy. Here we combine air quality modelling of fine particulate matter (PM2.5) over IGP cities, with meteorology, fire and smoke emissions data to directly test this hypothesis. Our analysis of satellite-derived agricultural fires shows that an approximate 10 d shift in the timing of NW India post-monsoon residue burning occurred since the introduction of the 2009 groundwater preservation policy. For the air quality crisis of 2016, we found that NW Indian CRB timing shifts made a small contribution to worsening air quality (3% over Delhi) during the post-monsoon season. However, if the same agricultural fires were further delayed, air quality in the CRB source region (i.e. Ludhiana) and for Delhi could have deteriorated by 30% and 4.4%, respectively. Simulations for other years highlight strong inter-annual variabilities in the impact of these timing shifts, with the magnitude and even direction of PM2.5 concentration changes strongly dependent on specific meteorological conditions. Overall we find post-monsoon IGP air quality to be far more sensitive to meteorology and the amount of residue burned in the fields of NW India than to the timing shifts in residue burning. Our study calls for immediate actions to provide farmers affordable and sustainable alternatives to residue burning to hasten its effective prohibition, which is paramount to reducing the intensity of post-monsoon IGP air pollution episodes.

In this work we have validated Copernicus Aerosol Monitoring Service (CAMS) derived aerosol optical depth (AOD) at four locations (Kanpur, Gandhi College, Jaipur and Lahore) in the Indo-Gangetic Basin and used it to study the aerosol climatology and trend in AOD at these locations. Lahore and Kanpur are urban and industrial sites with agricultural activity in the neighbouring regions. Gandhi College is in a rural agricultural area, whereas Jaipur is a desert dust source area. Aerosol climatology at the four sites are examined with MODIS-derived NDVI and ESA-CCI derived soil moisture data. CAMS-derived AOD for black carbon, sulphate, dust, sea salt and organic matter at the four sites are studied and discussed. It is observed that sulphate AOD has the largest influence on the total aerosol climatology. Contribution from dust and sea salt aerosols is observed only during pre-monsoon and monsoon seasons, whereas non-zero AOD is observed for organic matter, black carbon and sulphate aerosols throughout the year at all sites. Comparison of CAMS AOD with AERONET AOD shows better correlation when aerosol climatology is dominated by coarse particles as compared to when it is dominated by fine particles (e.g., at Kanpur, R2pre-monsoon = 0.63 and R2winter = 0.36). Trend analysis shows largest increase in organic matter (e.g., 0.305 ± 0.021 per year at Kanpur) and least in sea salt aerosols (e.g., 0.008 ± 0.001 per year at Kanpur).

National Capital Region (NCR) encompassing New Delhi is one of the most polluted urban metropolitan areas in the world. Real-time chemical characterization of fine particulate matter (PM1 and PM2.5) was carried out using three aerosol mass spectrometers, two aethalometers, and one single particle soot photometer (SP2) at two sites in Delhi (urban) and one site located ~40 km downwind of Delhi, during January–March 2018. The campaign mean PM2.5 (NR-PM2.5 + BC) concentrations at the two urban sites were 153.8 ± 109.4 μg.m−3 and 127.8 ± 83.2 μg.m−3, respectively, whereas PM1 (NR-PM1 + BC) was 72.3 ± 44.0 μg.m−3 at the downwind site. PM2.5 particles were composed mostly of organics (43–44)% followed by chloride (14–17)%, ammonium (9–11)%, nitrate (9%), sulfate (8–10)%, and black carbon (11–16)%, whereas PM1 particles were composed of 47% organics, 13% sulfate as well as ammonium, 11% nitrate as well as chloride, and 5% black carbon. Organic aerosol (OA) source apportionment was done using positive matrix factorization (PMF), solved using an advanced multi-linear engine (ME-2) model. Highly mass-resolved OA mass spectra at one urban and downwind site were factorized into three primary organic aerosol (POA) factors including one traffic-related and two solid-fuel combustion (SFC), and three oxidized OA (OOA) factors. Whereas unit mass resolution OA at the other urban site was factorized into two POA factors related to traffic and SFC, and one OOA factor. OOA constituted a majority of the total OA mass (45–55)% with maximum contribution during afternoon hours ~(70–80)%. Significant differences in the absolute OOA concentration between the two urban sites indicated the influence of local emissions on the oxidized OA formation. Similar PM chemical composition, diurnal and temporal variations at the three sites suggest similar type of sources affecting the particulate pollution in Delhi and adjoining cities, but variability in mass concentration suggest more local influence than regional.