PM2.5 pollution is threatening human health and quality of life, especially in some densely populated regions of Asia and Africa. This paper used remotely sensed annual mean PM2.5 concentrations to explore the spatiotemporal evolution of global continental PM2.5 pollution from 2000 to 2014. The work employed an improved Bayesian space-time hierarchy model combined with a multiscale homogeneous subdivision method. The statistical results quantitatively demonstrated a ‘high-value increasing and low-value decreasing’ trend. Areas with annual PM2.5 concentrations of more than 70μg/m3 and less than 10μg/m3 expanded, while areas with of an annual PM2.5 concentrations of 10–25μg/m3 shrank. The most heavily PM2.5-polluted areas were located in northwest Africa, where the PM2.5 pollution level was 12.0 times higher than the average global continental level; parts of China represented the second most PM2.5-polluted areas, followed by northern India and Saudi Arabia and Iraq in the Middle East region. Nearly all (96.50%) of the highly PM2.5-polluted area (hot spots) had an increasing local trend, while 68.98% of the lightly PM2.5-polluted areas (cold spots) had a decreasing local trend. In contrast, 22.82% of the cold spot areas exhibited an increasing local trend. Moreover, the spatiotemporal variation in the health risk from exposure to PM2.5 over the global continents was also investigated. Four areas, India, eastern and southern China, western Africa and central Europe, had high health risks from PM2.5 exposure. Northern India, northeastern Pakistan, and mid-eastern China had not only the highest risk but also a significant increasing trend; the areas of high PM2.5 pollution risk are thus expanding, and the number of affected people is increasing. Northern and central Africa, the Arabian Peninsula, the Middle East, western Russia and central Europe also exhibited increasing PM2.5 pollution health risks.

Rapid urbanization and economic growth has led to significant increase in municipal solid waste generation in India during the last few decades and its management has become a major issue because of poor waste management practices. Solid waste generated is deposited into open dumping sites with hardly any segregation and processing. Carbon dioxide (CO₂), methane (CH₄) and nitrous oxide (N₂O) are the major greenhouse gases that are released from the landfill sites due to the biodegradation of organic matter. In this present study, CH₄ and CO₂ emissions from a landfill in north-east India are estimated using a flux chamber during September, 2015 to August, 2016. The average emission rates of CH₄ and CO₂ are 68 and 92 mg/min/m², respectively. The emissions are highest in the summer whilst being lowest in winter. The diurnal variation of emissions indicated that the emissions follow a trend similar to temperature in all the seasons. Correlation coefficients of CH₄ and temperature in summer, monsoon and winter are 0.99, 0.87 and 0.97, respectively. The measured CH₄ in this study is in the range of other studies around the world. Modified Triangular Method (MTM), IPCC model and the USEPA Landfill gas emissions model (LandGEM) were used to predict the CH₄ emissions during the study year. The consequent simulation results indicate that the MTM, LandGEM-Clean Air Act, LandGEM-Inventory and IPCC models predict 1.9, 3.3, 1.6 and 1.4 times of the measured CH₄ emission flux in this study. Assuming that this higher prediction of CH₄ levels observed in this study holds well for other landfills in this region, a new CH₄ emission inventory (Units: Tonnes/year), with a resolution of 0.1⁰ × 0.1⁰ has been developed. This study stresses the importance of biodegradable composition of waste and meteorology, and also points out the drawbacks of the widely used landfill emission models.

Concentrations of heavy metals, as well as isotopic compositions of mercury (Hg) and lead (Pb), in mosses (Bryum argenteum) from the Three Gorges Reservoir (TGR) region were investigated to decipher the sources of atmospheric metals in this region. Higher contents of metals (0.90 ± 0.65 mg/kg of Cd, 24.6 ± 27.4 mg/kg of Cu, and 36.1 ± 51.1 mg/kg of Pb) in the mosses from TGR were found compared with those from pollution-free regions. Principal component analysis (PCA) grouped the moss metals into four main components which were associated with both anthropogenic and natural sources. The ratios of Pb isotopes of the mosses (1.153–1.173 for 206Pb/207Pb and 2.094–2.129 for 208Pb/206Pb) fell between those of the traffic emissions and coals. Similarly, the compositions of δ202Hg (−4.29∼-2.33‰) and Δ199Hg (within ±0.2‰) were comparable to those of the coals and coal combustion emissions from China and India. These joined results of Pb and Hg isotope data give solid evidences that the coal combustion and traffic emissions are the main causes of metal accumulation in the TGR region.

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 SO₄²⁻, 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 SO₄²⁻, 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%).

Exposure to fine particulate matter (PM₂.₅) is one of the leading risk factors for the mortality and morbidity burden in India. Health benefit expected from mitigation of emissions from individual sectors is the key policy information to address this issue. Here we quantify the relative shares of four major year-round anthropogenic sources to ambient PM₂.₅ in India using a chemical transport model and estimate premature deaths that could have been avoided due to complete mitigation of emissions from these sources at state level. Population-weighted all-India averaged (±1σ) annual ambient PM₂.₅ exposures due to residential, transport, industrial and energy sectors in 2010 are estimated to be 26.2 ± 12.5, 3.8 ± 4.3, 5.5 ± 2.7 and 2.2 ± 2.3 μg m⁻³, respectively. Complete mitigation of emissions from the transport, industrial and energy sectors combined would avoid 92,380 (95% uncertainty interval (UI), 40,918–140,741) premature deaths annually, primarily at the urban hotspots. For the residential sector, this would result in avoiding 378,295 (95% UI, 175,002–575,293) premature deaths due to a reduction in ambient PM₂.₅ exposure in addition to the benefit of avoiding all premature deaths from household exposure. Bihar and Goa are expected to have the largest (289) and smallest (48) premature mortality burden per 100,000 population due to anthropogenic PM₂.₅ exposure. From policy perspective, controlling residential sources should be prioritized in view of the effectiveness of implementing mitigation measures and the expected larger health benefit at a regional scale. However, additional mitigation measures are advised at the urban hotspots to curb emissions from the other sectors to get maximum possible health benefit.

While various investigations have been driven on polybrominated diphenyl ethers (PBDEs) and other flame retardants (FRs) in different framework around the world, information about contamination and fate of PBDEs and other FRs in developing countries especially in the Indian subcontinent is uncommon. Nepal being located in the Indian subcontinent, very little is known about contamination level of semi-volatile organic pollutants discharged into the environment. This motivated us to investigate the environmental fate of halogenated flame retardant (HFRs) in Nepalese condition. In this study, we investigated the concentration, fate, and sources of 9 PBDEs, 2 dechlorane plus isomers (DPs), and 6 novel brominated flame retardants (NBFRs). Moreover, air-soil exchange and soil–air partitioning were also evaluated to characterize the pattern of air-soil exchange and environmental fate. In general, the concentrations of NBFRs in soil were more prevalent than PBDEs and DPs, and accounted 95% of ∑HFRs. By and large, the concentrations of NBFRs and DPs were measured high in Kathmandu, while PBDEs level exceeded in Pokhara. Principal component analysis (PCA) study suggested contributions from commercial penta-, octa-, and deca-BDEs products and de-bromination of highly brominated PBDEs as the significant source of PBDEs. Likewise, low fₐₙₜᵢ ratio suggested DPs in soil might have originated from long-range atmospheric transport from remote areas, while high levels of decabromodiphenyl ethane (DBDPE) in soil were linked with the use of wide varieties of consumer products. The estimated fugacity fraction (ff) for individual HFR was quite lower (<0.05) than equilibrium value, suggesting that deposition and net transport from air to the soil is overwhelming. Soil-air partitioning study revealed neither octanol-air partition coefficient (KOA) nor black carbon partition coefficient (KBC₋A) is an appropriate surrogate for soil organic matter (SOM), subsequently, absorption by SOM has no or little role in the partitioning of HFRs.

Arsenic induced senescence (AIS) has been identified in the population of West Bengal, India very recently. Also there is a high incidence of arsenic induced peripheral neuropathy (PN) throughout India. However, the epigenetic regulation of AIS and its contribution in arsenic induced PN remains unexplored. We recruited seventy two arsenic exposed and forty unexposed individuals from West Bengal to evaluate the role of senescence associated miRNAs (SA-miRs) in AIS and their involvement if any, in PN. The downstream molecules of the miRNA associated with the disease outcome, was also checked by immuoblotting. In vitro studies were conducted with HEK 293 cells and sodium arsenite exposure. Our results show that all the SA-miRs were upregulated in comparison to unexposed controls. miR-29a was the most significantly altered, highest expression being in the arsenic exposed group with PN, suggesting its association with the occurrence of PN. We looked for the expression of peripheral myelin protein 22 (PMP22), a specific target of miR-29a associated with myelination and found that both in vitro and in vivo results showed over-expression of the protein. Since this was quite contrary to miRNA regulation, we checked for intermediate players β-catenin and GSK-3β upon arsenic exposure which affects PMP22 expression. We found that β-catenin was upregulated in vitro and was also highest in the arsenic exposed group with PN while GSK-3β followed the reverse pattern. Our findings suggest that arsenic exposure alters the expression of SA-miRs and the mir-29a/beta catenin/PMP22 axis might be responsible for arsenic induced PN.

Lijiang is a high-altitude city located on the eastern fringe of the Tibetan Plateau, with complex seasonal atmospheric circulations (i.e. westerly wind, Indian Monsoon, and East Asia Monsoon). Very few previous studies have focused on seasonal variations and sources of organic pollutants in Lijiang. In this study, a four-year air campaign from June 2009 to July 2013 was conducted to investigate the temporal trends and the sources of polycyclic aromatic hydrocarbons (PAHs) and organochlorine compounds [including organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs)]. The atmospheric PAH concentrations in winter are 2–3 times of those in summer, probably because of the combined result of enhanced local emission and long-range atmospheric transport (LRAT) during winter. Traffic pollution was the primary local source of PAHs, while biomass burning is the dominant LRAT source. OCPs and PCBs also mainly underwent LRAT to reach Lijiang. The peak concentrations of most of OCPs occurred in pre-monsoon season and winter, which were carried by air masses from Myanmar and India through westerly winds. As compared with other sites of the Tibetan Plateau, without the direct barrier of the Himalaya, Lijiang is easily contaminated by the incursion of polluted air masses.

Benzene, toluene and xylene (BTX) belong to an important group of aromatic volatile organic compounds (VOCs) that are usually emitted from various sources. BTX play a vital role in the tropospheric chemistry as well as pose health hazard to human beings. Thus, an investigation of ambient benzene, toluene and xylene (BTX) was conducted at urban and rural sites of Gorakhpur for a span of one year in order to ascertain the contamination levels. The sampling of BTX was performed by using a low-flow SKC Model 220 sampling pump equipped with activated coconut shell charcoal tubes with a flow rate of 250 ml/min for 20–24 h. The analysis was in accordance with NIOSH method 1501. The efficiency of pump was checked weekly using regulated rotameters with an accuracy of ±1%. The samples were extracted with CS₂ with occasional agitation and analyzed by GC-FID. The total BTX concentration ranged from 3.4 μg m⁻³ to 45.4 μg m⁻³ with mean value 30.95 μg m⁻³ and median 24.8 μg m⁻³. The mean concentration of total BTX was maximum during winter (39.3 μg m⁻³), followed by summer (28.4 μg m⁻³) and monsoon season (25.1 μg m⁻³). The mean concentration of BTX at urban site (11.8 μg m⁻³) was higher than that at rural site (8.8 μg m⁻³). At both the sites, T/B and X/B ratios were highest in monsoon and lowest in winters. Toluene against benzene plot shows R² value of 0.96 and 0.49 at urban and rural sites respectively. Higher R² value at urban site clearly indicates similar sources of emission for benzene and toluene. At both the sites, the estimated integrated lifetime cancer risk (ILTCR) for benzene exceeded the threshold value of 1E-06 whereas the individual hazard quotients (HQ) for BTX did not exceed unity at any of the sites.

Aerosol samples (as PM₁₀, n = 250) were collected from three rural/remote receptor locations in the mid Brahmaputra plain region and were chemically characterized for metals (Al, Fe, Co, Cu, Cr, Cd, Mn, Ni, Pb), ions (Ca²⁺, Mg²⁺, Na⁺, K⁺, NH₄⁺, F⁻, Cl⁻, NO₃⁻, SO₄²⁻), and carbon. Vital ratios like NO₃⁻/SO₄²⁻, EC/OC, K⁺/EC, K⁺/OC, enrichment factors and inter-species correlations were exploited to appreciate possible sources of aerosol. These empirical analyses pointed towards anthropogenic contributions of aerosol, particularly from biomass burning, vehicular emission, and road dust. The chemically characterized concentration data were subsequently fed into two receptor models viz. Principal Component Analysis-Multiple Linear Regression (PCA-MLR) and Chemical Mass Balance (CMB) for apportionment of sources of aerosol. The PCA-MLR estimates identified that the combustion sources together accounted for ∼42% of aerosol and the contribution of secondary formation to be 24%. Road and crustal dusts have been well apportioned by PCA-MLR, which together accounts for ∼26% of the aerosol. The CMB model estimates explained that the combustion sources taken together contributed ∼47% to the aerosol, which includes biomass burning (27%), vehicular emission (13%), coal (1%), kerosene (4%), and petroleum refining (2%). Other major sources that were apportioned were road dust (15%), crustal dust (26%), and construction dust (6%). There are inherent limitations in the source strength estimations because of uncertainty present in the source emission profiles that have been applied to the remote location of India. However, both the models (PCA-MLR and CMB) estimated the contribution of combustion sources to 42 and 47% respectively, which is comparable.