Classification of aerosols using particle linear depolarization ratio (PLDR) over seven urban locations of Asia

Active lidar remote sensing has been used to obtain detailed and quantitative information about the properties of aerosols. We have analyzed the spatio-temporal classification of aerosols using the parameters of particle linear depolarization ratio and single scattering albedo from Aerosol Robotic Network (AERONET) over seven megacities of Asia namely; Lahore, Karachi, Kanpur, Pune, Beijing, Osaka, and Bandung. We find that pollution aerosols dominate during the winter season in all the megacities. The concentrations, however, vary concerning the locations, i.e., 70-80% pollution aerosols are present over Lahore, 40-50% over Karachi, 90-95% over Kanpur and Pune, 60-70% and over Beijing and Osaka. Pure Dust (PD), Pollution Dominated Mixture (PDM), and Dust Dominated Mixture (DDM) are found to be dominant during spring and summer seasons.This proposes that dust over Asia normally exists as a mixture with pollution aerosols instead of pure form. We also find that black carbon (BC) dominated pollution aerosols.

Evaluation of spatial and temporal heterogeneity of black carbon aerosol mass concentration over India using three year measurements from IMD BC observation network

Extensive measurements of equivalent black carbon (EBC) aerosol mass concentration at fifteen stations of India Meteorological Department (IMD) BC observation network during the period 2016-2018 are used to study the spatial and temporal heterogeneity over India. The sampling sites represent different geographical region of India. Spatial distribution shows higher values of EBC over stations of north India and IGP. Highest annual mean EBC mass concentration during study period was reported at two mega cities New Delhi (13,575 ± 8401 ng/m³) followed by Kolkata (12,082 ± 6850 ng/m³) whereas lowest mean concentration was at Ranichauri (1737 ± 884 ng/m³) followed by Bhuj (2021 ± 1471 ng/m³). Stations located in coastal region of south India reported low concentration of EBC. In order to find out the quantitative contribution of biomass burning (EBC_BB) and fossil fuel (EBC_FF) in total mass concentration of EBC, source apportionment study has been carried out using Aethalometer model. The EBC_FF is the dominant contributor to EBC mass concentration at all the sites in every season, while the highest seasonal biomass burning mass contribution (37%) was observed in the winter at a background site Ranichauri. Maximum concentration of EBC_BB was observed at Srinagar (2671 ng/m³) where as EBC_FF was maximum in Delhi (11,074 ng/m³). Seasonal and diurnal variation studies have also been carried out for all the stations. The EBC mass concentrations exhibited strong seasonality, with the highest values occurring in postmonsoon/winter and the lowest in monsoon season. The higher EBC concentration in postmonsoon/winter seasons was attributed to the increased use of fuel in seasonal emission sources, domestic heating and stagnant meteorological conditions, whereas the low levels in monsoon season were related to the precipitation scavenging. Maximum concentration of EBC (22,409 ± 10,510 ng/m³) was observed in winter season over Kolkata. Our study finds high spatial heterogeneity in EBC concentrations across the study area.

An advanced three-way factor analysis model (SDABB model) for size-resolved PM source apportionment constrained by size distribution of chemical species in source profiles

Source samples including crustal dust, cement dust, coal combustion were sampled and ambient samples of PM_2.5 and PM₁₀ were synchronously collected in Hefei from April to December 2014. The size distributions of the markers in the measured source profiles were incorporated into ME-2 solution to develop a new method, called the SDABB model (an advanced ABB three-way factor analysis model incorporating size distribution information). The performance of this model was investigated using three-way synthetic and ambient dataset. For the synthetic tests, the size distributions of markers estimated by the SDABB model were more consistent with true condition. The AAEs between estimated and observed contributions of the SDABB ranged from 15.2% to 29.0% for PM₁₀ and 19.9%-31.6% for PM_2.5, which is lower than those of PMF2. For the ambient PM, six source categories were identified by SDABB for both sizes, although the profiles were different. The source contributions were sulphate (33.33% and 24.53%), nitrate and SOC (22.33% and 18.16%), coal combustion (19.01% and 18.23%), vehicular exhaust (12.99% and 12.07%), crustal dust (10.69% and 19.40%) and cement dust (1.65% and 5.39%) for PM_2.5 and PM₁₀ respectively. In addition, the estimated ratios of Al, Si, Ti and Fe in CRD were 0.76, 0.84, 1.10 and 0.85; those of Al and Si in CC were 0.42 and 0.66; Ca and Si in CD were 0.95 and 1.10; NO₃^- and NH₄⁺ in nitrate were 1.11 and 1.01; and SO₄^2- and NH₄⁺ in sulphate were 0.96 and 1.16. These modeled ratios were consistent with the measured ratios. The size distribution of contributions also came close to reality. Thus, the advanced SDABB three-way model can better capture the characteristics of sources between sizes by effectively incorporating the size distributions of the markers as physical constraints.

Development of a Regression Model for Estimating Daily Radiative Forcing Due to Atmospheric Aerosols from Moderate Resolution Imaging Spectrometers (MODIS) Data in the Indo Gangetic Plain (IGP)

The assessment of direct radiative forcing due to atmospheric aerosols (ADRF) in the Indo Gangetic Plain (IGP), which is a food basket of south Asia, is important for measuring the effect of atmospheric aerosols on the terrestrial ecosystem and for assessing the effect of aerosols on crop production in the region. Existing comprehensive analytical models to estimate ADRF require a large number of input parameters and high processing time. In this context, here, we develop a simple model to estimate daily ADRF at any location on the surface of the IGP through multiple regressions of AErosol RObotic NETwork (AERONET) aerosol optical depth (AOD) and atmospheric water vapour using data from 2002 to 2015 at 10 stations in the IGP. The goodness of fit of the model is indicated by an adjusted R2 value of 0.834. The Jackknife method of deleting one group (station data) was employed to cross validate and study the stability of the regression model. It was found to be robust with an adjusted R2 fluctuating between 0.813 and 0.842. In order to use the year-round ADRF model for locations beyond the AERONET stations in the IGP, AOD, and atmospheric water vapour products from MODIS Aqua and Terra were compared against AERONET station data and they were found to be similar. Using MODIS Aqua and Terra products as input, the year-round ADRF regression was evaluated at the IGP AERONET stations and found to perform well with Pearson correlation coefficients of 0.66 and 0.65, respectively. Using ADRF regression model with MODIS inputs allows for the estimation of ADRF across the IGP for assessing the aerosol impact on ecosystem and crop production.

Chemical composition and source-apportionment of sub-micron particles during wintertime over Northern India: New insights on influence of fog-processing

A comprehensive study was carried out from central part of Indo-Gangetic Plain (IGP; at Kanpur) to understand abundance, temporal variability, processes (secondary formation and fog-processing) and source-apportionment of PM₁-bound species (PM₁: particulate matter of aerodynamic diameter ≤ 1.0 μm) during wintertime. A total of 50 PM₁ samples were collected of which 33 samples represent submicron aerosol characteristics under non-foggy condition whereas 17 samples represent characteristics under thick foggy condition. PM₁ mass concentration during non-foggy episodes varied from 24-393 (Avg.: 247) μg m^-3, whereas during foggy condition it ranged from 42-243 (Avg.: 107) μg m^-3. With respect to non-foggy condition, the foggy conditions were associated with higher contribution of PM₁-bound organic matter (OM, by 23%). However, lower fractional contribution of SO₄^2-, NO₃^- and NH₄⁺ during foggy conditions is attributable to wet-scavenging owing to their high affinity to water. Significant influence of fog-processing on organic aerosols composition is also reflected by co-enhancement in OC/EC and WSOC/OC ratio during foggy condition. A reduction by 5% in mineral dust fraction under foggy condition is associated with a parallel decrease in PM₁ mass concentration. However, mass fraction of elemental carbon (EC) looks quite similar (≈3% of PM₁) but the mass absorption efficiency (MAE) of EC is higher by 30% during foggy episodes. Thus, it is evident from this study that fog-processing leads to quite significant enhancement in OM (23%) contribution (and MAE of EC) with nearly equal and parallel decrease in SO₄^2-, NO₃^- and NH₄⁺ and mineral dust fractions (totaling to 24%). Characteristic features of mineral dust remain similar under foggy and non-foggy conditions; inferred from similar ratios of Fe/Al (≈0.3), Ca/Al (0.35) and Mg/Al (0.22). Positive matrix factorization (PMF) resolves seven sources: biomass burning (19.4%), coal combustion (1.1%), vehicular emission (3%), industrial activities (6.1%), leather tanneries (4%), secondary transformations (46.2%) and mineral dust (20.2%).

Importance of aerosol non-sphericity in estimating aerosol radiative forcing in Indo-Gangetic Basin

Aerosols are usually presumed spherical in shape while estimating the direct radiative forcing (DRF) using observations or in the models. In the Indo-Gangetic Basin (IGB), a regional aerosol hotspot where dust is a major aerosol species and has been observed to be non-spherical in shape, it is important to test the validity of this assumption. We address this issue using measured chemical composition at megacity Delhi, a representative site of the western IGB. Based on the observation, we choose three non-spherical shapes - spheroid, cylinder and chebyshev, and compute their optical properties. Non-spherical dust enhances aerosol extinction coefficient (β_ext) and single scattering albedo (SSA) at visible wavelengths by >0.05km^-1 and >0.04 respectively, while it decreases asymmetry parameter (g) by ~0.1. Accounting non-sphericity leads top-of-the-atmosphere (TOA) dust DRF to more cooling due to enhanced backscattering and increases surface dimming due to enhanced β_ext. Outgoing shortwave flux at TOA increases by up to 3.3% for composite aerosols with non-spherical dust externally mixed with other spherical species. Our results show that while non-sphericity needs to be accounted for, choice of shape may not be important in estimating aerosol DRF in the IGB.

One year record of bioaerosols and particles concentration in Indo-Gangetic Plain: Implications of biomass burning emissions to high-level of endotoxin exposure

Previous studies worldwide have suggested the potential role of bioaerosols as ice-nuclei and cloud-condensation nuclei. Furthermore, their participation in regulating the global carbon cycle urges systematic studies from different environmental conditions throughout the globe. Towards this through one-year study, conducted from June 2015-May 2016, we report on atmospheric abundance and variability of viable bioaerosols, organic carbon (OC) and particles number and deduced mass concentrations from Indo-Gangetic Plain (IGP; at Kanpur). Among viable bioaerosols, the highest concentrations of Gram-positive bacteria (GPB), Gram-negative bacteria (GNB) and Fungi were recorded during December-January (Avg.: 189 CFU/m³), November (244 CFU/m³) and September months (188 CFU/m³), respectively. Annual average concentration of GPB, GNB and Fungi were 105 ± 58, 144 ± 82 and 116 ± 51 CFU/m³. Particle number concentration (PNC) associated with fine-fraction aerosols (FFA) predominates throughout the year. However, mineral dust (coarser particle) remains a perennial constituent of atmospheric aerosols over the IGP. Temporal variability records and significant positive linear relationship (p < 0.05) of GPB and GNB with OC and biomass burning derived potassium (K⁺_BB) indicates their association with massive emissions from paddy-residue burning (PRB) and bio-fuel burning. Influence of meteorological parameters on viable bioaerosols abundance has been rigorously investigated herein. Accordingly, ambient temperature seems to be more affecting the bacteria (anti-correlation), whereas wet-precipitation (1-4 mm) relates to higher abundance of Fungi. High abundance of GNB during large-scale biomass burning emissions has implications to endotoxin exposure on human health. Field-based data-set of bioaerosols, OC, PNC and deduced mass concentrations reported herein could serve to better constraint their role in human health and climate relevance.

Fine particulates over South Asia: Review and meta-analysis of PM2.5 source apportionment through receptor model

Fine particulates (PM_2.5) constitute dominant proportion of airborne particulates and have been often associated with human health disorders, changes in regional climate, hydrological cycle and more recently to food security. Intrinsic properties of particulates are direct function of sources. This initiates the necessity of conducting a comprehensive review on PM_2.5 sources over South Asia which in turn may be valuable to develop strategies for emission control. Particulate source apportionment (SA) through receptor models is one of the existing tool to quantify contribution of particulate sources. Review of 51 SA studies were performed of which 48 (94%) were appeared within a span of 2007-2016. Almost half of SA studies (55%) were found concentrated over few typical urban stations (Delhi, Dhaka, Mumbai, Agra and Lahore). Due to lack of local particulate source profile and emission inventory, positive matrix factorization and principal component analysis (62% of studies) were the primary choices, followed by chemical mass balance (CMB, 18%). Metallic species were most regularly used as source tracers while use of organic molecular markers and gas-to-particle conversion were minimum. Among all the SA sites, vehicular emissions (mean ± sd: 37 ± 20%) emerged as most dominating PM_2.5 source followed by industrial emissions (23 ± 16%), secondary aerosols (22 ± 12%) and natural sources (20 ± 15%). Vehicular emissions (39 ± 24%) also identified as dominating source for highly polluted sites (PM_2.5>100 μgm^-3, n = 15) while site specific influence of either or in combination of industrial, secondary aerosols and natural sources were recognized. Source specific trends were considerably varied in terms of region and seasonality. Both natural and industrial sources were most influential over Pakistan and Afghanistan while over Indo-Gangetic plain, vehicular, natural and industrial emissions appeared dominant. Influence of vehicular emission was found single dominating source over southern part while over Bangladesh, both vehicular, biomass burning and industrial sources were significant.

Columnar aerosol characteristics and radiative forcing over the Doon Valley in the Shivalik range of northwestern Himalayas

Spectral aerosol optical depth (AOD) measurements obtained from multi-wavelength radiometer under cloudless conditions over Doon Valley, in the foothills of the western Himalayas, are analysed during the period January 2007 to December 2012. High AOD values of 0.46 ± 0.08 and 0.52 ± 0.1 at 500 nm, along with low values of Ångström exponent (0.49 ± 0.01 and 0.44 ± 0.03) during spring (March-May) and summer (June-August), respectively, suggest a flat AOD spectrum indicative of coarse-mode aerosol abundance compared with winter (December-February) and autumn (September-November), which are mostly dominated by fine aerosols from urban/industrial emissions and biomass burning. The columnar size distributions (CSD) retrieved from the King's inversion of spectral AOD exhibit bimodal size patterns during spring and autumn, while combinations of the power-law and unimodal distributions better simulate the retrieved CSDs during winter and summer. High values of extinction coefficient near the surface (∼0.8-1.0 km^-1 at 532 nm) and a steep decreasing gradient above are observed via CALIPSO profiles in autumn and winter, while spring and summer exhibit elevated aerosol layers between ∼1.5 and 3.5 km due to the presence of dust. The particle depolarisation ratio shows a slight increasing trend with altitude, with higher values in spring and summer indicative of non-spherical particles of dust origin. The aerosol-climate implications are evaluated via the aerosol radiative forcing (ARF), which is estimated via the synergy of OPAC and SBDART models. On the monthly basis, the ARF values range from ∼ -30 to -90 W m^-2 at the surface, while aerosols cause an overall cooling effect at the top of atmosphere (approx. -5 to -15 W m^-2). The atmospheric heating via aerosol absorption results in heating rates of 1.2-1.6 K day^-1 during March-June, which may contribute to changes in monsoon circulation over northern India and the Himalayas.

Statistical variability comparison in MODIS and AERONET derived aerosol optical depth over Indo-Gangetic Plains using time series modeling

A lot of studies in the literature of Aerosol Optical Depth (AOD) done by using Moderate Resolution Imaging Spectroradiometer (MODIS) derived data, but the accuracy of satellite data in comparison to ground data derived from ARrosol Robotic NETwork (AERONET) has been always questionable. So to overcome from this situation, comparative study of a comprehensive ground based and satellite data for the period of 2001-2012 is modeled. The time series model is used for the accurate prediction of AOD and statistical variability is compared to assess the performance of the model in both cases. Root mean square error (RMSE), mean absolute percentage error (MAPE), stationary R-squared, R-squared, maximum absolute percentage error (MAPE), normalized Bayesian information criterion (NBIC) and Ljung-Box methods are used to check the applicability and validity of the developed ARIMA models revealing significant precision in the model performance. It was found that, it is possible to predict the AOD by statistical modeling using time series obtained from past data of MODIS and AERONET as input data. Moreover, the result shows that MODIS data can be formed from AERONET data by adding 0.251627 ± 0.133589 and vice-versa by subtracting. From the forecast available for AODs for the next four years (2013-2017) by using the developed ARIMA model, it is concluded that the forecasted ground AOD has increased trend.

Contribution of Brown Carbon to Direct Radiative Forcing over the Indo-Gangetic Plain

The Indo-Gangetic Plain is a region of known high aerosol loading with substantial amounts of carbonaceous aerosols from a variety of sources, often dominated by biomass burning. Although black carbon has been shown to play an important role in the absorption of solar energy and hence direct radiative forcing (DRF), little is known regarding the influence of light absorbing brown carbon (BrC) on the radiative balance in the region. With this in mind, a study was conducted for a one month period during the winter-spring season of 2013 in Kanpur, India that measured aerosol chemical and physical properties that were used to estimate the sources of carbonaceous aerosols, as well as parameters necessary to estimate direct forcing by aerosols and the contribution of BrC absorption to the atmospheric energy balance. Positive matrix factorization analyses, based on aerosol mass spectrometer measurements, resolved organic carbon into four factors including low-volatile oxygenated organic aerosols, semivolatile oxygenated organic aerosols, biomass burning, and hydrocarbon like organic aerosols. Three-wavelength absorption and scattering coefficient measurements from a Photo Acoustic Soot Spectrometer were used to estimate aerosol optical properties and estimate the relative contribution of BrC to atmospheric absorption. Mean ± standard deviation values of short-wave cloud free clear sky DRF exerted by total aerosols at the top of atmosphere, surface and within the atmospheric column are -6.1 ± 3.2, -31.6 ± 11, and 25.5 ± 10.2 W/m(2), respectively. During days dominated by biomass burning the absorption of solar energy by aerosols within the atmosphere increased by ∼35%, accompanied by a 25% increase in negative surface DRF. DRF at the top of atmosphere during biomass burning days decreased in negative magnitude by several W/m(2) due to enhanced atmospheric absorption by biomass aerosols, including BrC. The contribution of BrC to atmospheric absorption is estimated to range from on average 2.6 W/m(2) for typical ambient conditions to 3.6 W/m(2) during biomass burning days. This suggests that BrC accounts for 10-15% of the total aerosol absorption in the atmosphere, indicating that BrC likely plays an important role in surface and boundary temperature as well as climate.

Variability in radiative properties of major aerosol types: a year-long study over Delhi--an urban station in Indo-Gangetic Basin

Aerosol measurements over an urban site at Delhi in the western Ganga basin, northern India, were carried out during 2009 using a ground-based automatic sun/sky radiometer to identify their different types and to understand their possible radiative implications. Differentiation of aerosol types over the station was made using the appropriate thresholds for size-distribution of aerosols (i.e. fine-mode fraction, FMF at 500 nm) and radiation absorptivity (i.e. single scattering albedo, SSA at 440 nm). Four different aerosol types were identified, viz., polluted dust (PD), polluted continent (PC), mostly black carbon (MBC) and mostly organic carbon (MOC), which contributed ~48%, 32%, 11% and 9%, respectively to the total aerosols. Interestingly, the optical properties for these aerosol types differed considerably, which were further used, for the first time, to quantify their radiative implications over this station. The highest atmospheric forcing was observed for PC aerosol type (about +40 W m(-2), along with the corresponding atmospheric heating rate of 1.10 K day(-1)); whereas the lowest was for MBC aerosol type (about +25 W m(-2), along with the corresponding atmospheric heating rate of 0.69 K day(-1)).