COVID-19 lockdowns reduce the Black carbon and polycyclic aromatic hydrocarbons of the Asian atmosphere: source apportionment and health hazard evaluation

Vehicular mediated emissions of polycyclic aromatic hydrocarbons in roadside soils of Shanghai

This study evaluates the impact of vehicular emissions on polycyclic aromatic hydrocarbons (PAHs) in roadside soils in Shanghai during the COVID-19 lockdown period. Soil samples from roadside lawns were collected, with PAH concentrations ranging from 153 to 5639 ng g-1. A significant reduction in PAH levels compared to their pre-COVID-19 levels (Kruskal-Wallis H test, p < 0.05) was observed in surface soil samples, highlighting the contribution of traffic and coal combustion to urban pollution. Source identification, using molecular diagnostic ratios and principal component analysis, revealed that vehicular emissions were the primary contributors to PAHs in Shanghai's roadside soils. The toxic equivalent quantity for benzo[a]pyrene concentrations in the soil samples was associated with these sources. The incremental lifetime cancer risk model indicated that adult exposure to PAHs in the soil posed health risks greater than 10-6 but lower than 10-4, suggesting a low-risk level. These findings suggest that targeted measures in the transportation sector could improve urban soil quality and reduce associated health risks.

Estimation of gaseous polycyclic aromatic hydrocarbons (PAHs) and characteristics of atmospheric PAHs at a traffic site in Kanazawa, Japan

Size-fractionated particulate matter (PM2.5 and PM>2.5) was collected at a traffic site in Kanazawa, Japan in a seasonal sampling work in 2020. Nine polycyclic aromatic hydrocarbons (4- to 6-ring PAHs) were determined in fine and coarse particles. The gas/particle partitioning coefficients (Kp) of the PAHs were calculated from the supercooled liquid vapour pressure and octanol-air partitioning coefficient based on the relationships obtained in previous traffic pollution-related studies. Gaseous PAHs were estimated by Kp and the concentrations of PM and particulate PAHs. The concentrations of total PAHs were 32.5, 320.1 and 5646.2 pg/m3 in the PM>2.5, PM2.5 and gas phases, respectively. Significant seasonal trends in PAHs were observed (particle phase: lowest in summer, gas phase: lowest in spring, particle and gas phase: lowest in spring). Compared to 2019, the total PAH concentrations (in particles) decreased in 2020, especially in spring and summer, which might be due to reduced traffic trips during the COVID-19 outbreak. The incremental lifetime cancer risk (ILCR) calculated from the toxic equivalent concentrations relative to benzo[a]pyrene (BaPeq) was lower than the acceptable limit issued by the US Environmental Protection Agency, indicating a low cancer risk in long-term exposure to current PAH levels. It is notable that gaseous PAHs considerably contributed to BaPeq and ILCR (over 50%), which highlighted the significance of gaseous PAH monitoring for public health protection. This low-cost estimation method for gaseous PAHs can be expected to reliably and conveniently obtain PAH concentrations as a surrogate for traditional sampling in the future work.

Impacts of COVID-19 lockdown on PM2.5-bound polycyclic aromatic hydrocarbons in Hohhot, Northern China: characteristics, sources, and source-specific health risks

Quantifying the impacts of reduction strategies on PM2.5-bound polycyclic aromatic hydrocarbons (PAHs) is essential for reducing the health risks of PM2.5. The COVID-19 lockdown provided an opportunity to reveal the quantitative relationship between lockdown measures and the health risks of PAHs. In this study, the characteristics, sources, and health risks of PAHs were investigated during the COVID-19 lockdown in Hohhot. The source-specific health risks of PAHs were assessed using a combination of incremental lifetime cancer risk models (ILCR) and positive matrix factorization (PMF). Compared with the pre-LD period (pre-LD, 87.41 ± 5.98 ng·m-3), the total concentration of ∑PAHs during the lockdown period (LD, 32.52 ± 2.31 ng·m-3) decreased by 62.8% in Hohhot. Coal combustion (51.5%), gasoline emissions (21.9%), diesel emissions (12.9%), industrial emissions (9.3%), and biomass burning (4.7%) were the predominant sources of PAHs in Hohhot. Except for male children, the ILCR of all groups exceeded the threshold for high health risks (1 × 10- 4). Dermal contact is the predominant exposure pathway for carcinogenic risk. Compared with the pre-LD period, the ILCR values decreased by 62.5-62.7% during the LD period. The PMF-ILCR results indicated that industrial emissions (29.1%), coal combustion (28.4%), and diesel emissions (18.5%) were the main sources of ∑ILCR. A Monte Carlo simulation revealed that the cumulative carcinogenic risks at the 95th percentile of the six groups were 1.5-6.3 times the threshold of high health risk (1 × 10- 4). These results emphasize that regulating industrial emissions and coal combustion is effective in reducing carcinogenic risks in industrial cities with large coal consumption.

Evaluating a river's ecological health: A multidimensional approach

Evaluating the health of river surface water is essential, as rivers support significant biological resources and serve as vital drinking water sources. While the Water Quality Index (WQI) is commonly employed to evaluate surface water quality, it fails to consider biodiversity and does not fully capture the ecological health of rivers. Here we show a comprehensive assessment of the ecological health of surface water in the lower Yangtze River (LYR), integrating chemical and biological metrics. According to traditional WQI metrics, the LYR's surface water generally meets China's Class II standards. However, it also contains 43 high-risk emerging contaminants; nitrobenzenes are found at the highest concentrations, representing 25-90% of total detections, while polycyclic aromatic hydrocarbons present the most substantial environmental risks, accounting for 81-93% of the total risk quotient. Notably, the plankton-based index of biological integrity (P-IBI) rates the ecological health of the majority of LYR water samples (59.7%) as 'fair', with significantly better health observed in autumn compared to other seasons (p < 0.01). Our findings suggest that including emerging contaminants and P-IBI as additional metrics can enhance the traditional WQI analysis in evaluating surface water's ecological health. These results highlight the need for a multidimensional assessment approach and call for improvements to LYR's ecological health, focusing on emerging contaminants and biodiversity rather than solely on reducing conventional indicators.

Associations between atmospheric PM2.5 exposure and carcinogenic health risks: Surveillance data from the year of lowest recorded levels in Beijing, China

Scant research has pinpointed the year of minimum PM2.5 concentration through extensive, uninterrupted monitoring, nor has it thoroughly assessed carcinogenic risks associated with analyzing numerous components during this nadir in Beijing. This study endeavored to delineate the atmospheric PM2.5 pollution in Beijing from 2015 to 2022 and to undertake comprehensive evaluation of carcinogenic risks associated with the composition of atmospheric PM2.5 during the year exhibiting the lowest concentration. PM2.5 concentrations were monitored gradually in 9 districts of Beijing for 7 consecutive days per month from 2015 to 2022, and 32 kinds of PM2.5 components collected in the lowest PM2.5 concentration year were analyzed. This comprehensive dataset served as the basis for carcinogenic risk assessment using Monte Carlo simulation. And we applied the Positive Matrix Factorization (PMF) method to identity the sources of atmospheric PM2.5. Furthermore, we integrated this source appointment model with risk assessment model to discern the origins of these risks. The findings revealed that the annual average PM2.5 concentration in 2022 stood at 43.1 μg/m3, marking the lowest level recorded. The mean carcinogenic risks of atmospheric PM2.5 exposure calculated at 6.30E-6 (empirical 95% CI 1.09E-6 to 2.25E-5) in 2022. The PMF model suggested that secondary sources (35.4%), coal combustion (25.6%), resuspended dust (15.1%), biomass combustion (14.1%), vehicle emissions (7.1%), industrial emissions (2.0%) and others (0.7%) were the main sources of atmospheric PM2.5 in Beijing. The mixed model revealed that coal combustion (2.41E-6), vehicle emissions (1.90E-6) and industrial emissions (1.32E-6) were the main sources of carcinogenic risks with caution. Despite a continual decrease in atmospheric PM2.5 concentration in recent years, the lowest concentration levels still pose non-negligible carcinogenic risks. Notably, the carcinogenic risks associated with metals and metalloids exceeded that of PAHs. And the distribution of risk sources did not align proportionally with the distribution of PM2.5 mass concentration.

Impact of occupational noise exposure on the hearing level in hospital staffs: a longitudinal study

The study aimed to evaluate the impact of occupational noise on hearing loss among healthcare workers using audiometry. A longitudinal study was conducted with a six-month follow-up period in a hospital with 21 participants, divided into high-noise-exposure (HNE) and low-noise-exposure (LNE) groups. Mean noise levels were higher in the HNE group (70.4 ± 4.5 dBA), and hearing loss was measured using pure-tone audiometry at baseline and follow-up. The HNE group had significantly higher mean threshold levels at frequencies of 0.25 kHz, 0.5 kHz, 4.0 kHz, and an average of 0.5, 1, 2, and 4 kHz (all p-values < 0.05) after the follow-up period. After adjusting for confounding factors, the HNE group had significantly higher hearing loss levels at 0.25 kHz, 0.5 kHz, and average frequencies of 0.5, 1, 2, and 4 kHz compared to the LNE group at the second measurement. Occupational noise levels above 65 dBA over six months were found to cause significant threshold changes at frequencies of 0.25 kHz, 0.5 kHz, and an average of 0.5-4.0 kHz. This study highlights the risk of noise-induced hearing loss among healthcare workers and emphasizes the importance of implementing effective hearing conservation programs in the workplace. Regular monitoring and assessment of noise levels and hearing ability, along with proper use of personal protective equipment, are crucial steps in mitigating the impact of occupational noise exposure on the hearing health of healthcare workers.

New prospects of environmental RNA metabarcoding research in biological diversity, ecotoxicological monitoring, and detection of COVID-19: a critical review

Ecosystems are multifaceted and complex systems and understanding their composition is crucial for the implementation of efficient conservation and management. Conventional approaches to biodiversity surveys can have limitations in detecting the complete range of species present. In contrast, the study of environmental RNA (eRNA) offers a non-invasive and comprehensive method for monitoring and evaluating biodiversity across different ecosystems. Similar to eDNA, the examination of genetic material found in environmental samples can identify and measure many species, including ones that pose challenges to traditional methods. However, eRNA is degraded quickly and therefore shows promise in detection of living organisms closer to their actual location than eDNA methods. This method provides a comprehensive perspective on the well-being of ecosystems, facilitating the development of focused conservation approaches to save at-risk species and uphold ecological equilibrium. Furthermore, eRNA has been recognized as a valuable method for the identification of COVID-19 in the environment, besides its established uses in biodiversity protection. The SARS-CoV-2 virus, which is accountable for the worldwide epidemic, releases RNA particles into the surrounding environment via human waste, providing insights into the feasibility of detecting it in wastewater and other samples taken from the environment. In this article, we critically reviewed the recent research activities that use the eRNA method, including its utilization in biodiversity conservation, ecological surveillance, and ecotoxicological monitoring as well as its innovative potential in identifying COVID-19. Through this review, the reader can understand the recent developments, prospects, and challenges of eRNA research in ecosystem management and biodiversity conservation.

Integral trends in research of lead exposure and child health from 2012 to 2022: a bibliometric analysis

Lead poisoning in children is a non-negligible and ongoing threat to children's health and optimal development worldwide. There is no sufficient scientometric analysis available on this subject, though. Aiming to uncover the research development, hotspots, and possible future orientation, we performed a scientometric analysis of related publications from 2012 to 2022. Initial information was accessed using the "Analysis Results" and "Create Citation Report" sections of the Web of Science core collection database, which were utilized to find original publications in this field of research. Biblioshiny and VOSviewer software were applied to further analyze and visualize the data. The research addressed a range of topics, including yearly publications, highly cited articles, co-cited references, journals, authors, nations, organizations, and keywords. A total of 883 articles were retrieved. From 2018 to 2021, the annual publication output was abundant and peaked in 2019. Among 111 countries, the USA obtained the highest number of documents issued, total citations, and total link strength. Meanwhile, most of the top 15 institutions, including the top four, are located in the USA. Further, we spotted greater scopes with development potential, including enhancing records to lessen exposure to harmful risks, improving methods for observing lead sources, and elucidating the gradient link between lead poisoning symptoms and concentrations. We anticipate that our research will assist researchers in summarizing previous research and providing perspectives for workable prospective study topics.

Assessment of ambient air quality in relation to the burning of firecrackers during the festival of Diwali: A case study of Jodhpur City (India)

The study attempts to examine the impact of firework activities during Diwali Festival on ambient air quality of Jodhpur city. Air quality parameters particulate matter of diameter 10 μm (PM10), particulate matter of diameter 2.5 μm (PM2.5), sulfur dioxide (SO2), nitrogen dioxide (NO2) and heavy metals in PM2.5 like Pb, Ni, Ba, Al, As and Sr are monitored at two locations, for 15 days, starting from 7 days before the festival of Diwali, on the day of the festival (Diwali) and 7 days after Diwali. On the occasion of Diwali, it was discovered that the 24-h average levels of various pollutants were significantly elevated compared to regular days preceding the festival. Specifically, at the HBO site, the concentrations were notably increased, with sulfur dioxide (SO2) reaching 5.62 times higher, nitrogen dioxide (NO2) at 3 times higher, particulate matter of diameter 10 μm (PM10) at 2.35 times higher, and particulate matter of diameter 2.5 μm (PM2.5) at 1.01 times higher than the usual levels before Diwali. Similarly, at the PTMM site, there were substantial elevations in pollutant concentrations during Diwali compared to pre-festival days, with SO2 registering 2.53 times higher, NO2 at 2.37 times higher, PM2.5 at 1.9 times higher, and PM10 at 1.57 times higher levels than normal. Concentration of Al, Ba, Sr and Pb at HBO site and Al at PTMM site was highest on Diwali day. Air quality index which was in good category on normal days before Diwali, fell into poor category starting from the day before Diwali and remain in poor category on normal days after Diwali. The result indicates the worsening of ambient air quality during Diwali which can adversely impact the human health in terms of various respiratory complications.

Spatial distribution, source, and ecological risk of PAHs in the sediment of the Fenhe River Basin, China

Polycyclic aromatic hydrocarbons (PAHs) are persistent toxic substances that have ubiquitous presence in water, air, soil, and sediment environments. The growth of PAH toxicities and related ecotoxicology risk in sediment has been a serious concern. Present study examined the PAH concentration, sources, and ecological risk from the selected sites in sediment of Fenhe River. The characteristic molecular ratio (CMR) and principal component analysis (PCA) were applied to analyze the sources. The ecological risk assessment was conducted based on the sediment quality guidelines, the mean effects range median quotient, as well as the toxic equivalent quantity values. The results showed that the mean values of total contents of the 16 individual PAHs were 3.66 mg/kg and 3.16 mg/kg in wet and dry seasons, which were relatively high when compared with other rivers worldwide. Their spatial distribution presented the lower contents in the upstream, while higher concentrations in the middle and down streams of the river. The low molecular weight PAHs were major constituents, and 3-ring PAHs have the highest contents. The results of source analysis indicated that PAHs were primarily from the burning of oil, coal, and biomass. The ecological risk evaluations suggested that the possible adverse biological effects, the low to medium comprehensive risks, and the minor carcinogenic risks existed in the study area. This investigation might provide useful baseline data and technical support for policy-makers and researchers.

Combined assessment of health hazard and odour impact of soils at a contaminated site: a case study on a defunct pharmaceuticals factory in China

Surveys and assessments of contaminated sites primarily focus on hazardous pollutants in the soil with less attention paid to odorants. This makes the management of contaminated sites difficult. In this study, hazardous and odorous pollutants in the soil were assessed for a large site that was previously used for production of pharmaceuticals to determine the degree and characteristics of soil contamination at pharmaceutical production sites, for undertaking rational remediation measures. The main hazardous pollutants at the study site were triethylamine, n-butyric acid, benzo(a)pyrene (BaP), N-nitrosodimethylamine (NDMA), dibenzo(a,h)anthracene (DBA), total petroleum hydrocarbons (C10-C40) (TPH), and 1,2-dichloroethane; TEA, BA, and isovaleric acid (IC) were the main odorants. As the type and distribution of hazardous and odorous pollutants differ, it is necessary to separately assess the impact of these pollutants at a contaminated site. Soils in the surface layer pose significant non-carcinogenic (HI = 68.30) and carcinogenic risks (RT = 3.56E-5), whereas those in the lower layer only pose non-carcinogenic risks (HI > 7.43). Odorants were found at considerable concentrations both in the surface and lower layers, with the maximum concentrations being 29,309.91 and 41.27, respectively. The findings of this study should improve our understanding of soil contamination at former pharmaceutical production sites and should inform the assessment of the risks posed by contaminated sites, with problems associated with odour, and possible remediation strategies.

Ambient black carbon variations and emission characteristics of typical Chinese vessels in the Yangtze River Delta, China

Black carbon (BC) has a significant impact on air quality, climate change, and human health. Studies on BC from vessel exhaust have been focused on in recent years. To realize the contribution of BC from vessels to ambient air quality, 28 months of BC variation were observed from February 2019 to May 2022, including 3 fishing moratoriums and 2 normal periods. The results showed that the average daily concentration of BC in the fishing moratorium was significantly lower than that in the normal period. The difference proportion of the BC concentration between 370 and 880 nm was calculated over the whole period. As a result, the mean difference value in the fishing moratorium from February to May was 0.06 ± 0.07, and the normal period was -0.02 ± 0.05. The aethalometer model indicated that BC was greatly affected by fossil fuel combustion in the normal period. The effect of vessel emissions on regional BC concentrations was considerable. In addition, 16 PAHs and 21 elements in PM emitted from 24 vessels of different types were sampled and analyzed in Dianshan Lake and the Taipu River. EC accounted for the highest proportion (23.64%) in the sample of small trawlers compared to the emissions from cargo ships with large tonnages. The component profiles of vessel exhaust showed that Zn, As, phenanthrene (Phe), anthracene (Ant), fluoranthene (Fla), and pyrene (Pyr) were the dominant species, although some of these species were mainly recognized as characteristic factors of coal combustion. To improve the accuracy of identifying the vessel source, the diagnostic ratios of Ant/(Ant + Phe), BaA/(BaA + Chr), Phe/Ant, and BaA/Chr were provided, and they exhibited the obvious characteristics of fuel combustion.

Occupational health risk assessment of construction workers caused by particulate matter exposure on construction sites

Construction particulate matter is one of the main environmental impact factors in the construction process. Due to the lack of sufficient awareness and understanding of the potential health effects of particulate matter by project managers and construction workers, the on-site working environment has not been effectively improved for a long time, and construction workers have been exposed to high particulate matter concentration conditions for physical labor for a long time. The construction site is a special operation scene, and the source and diffusion of particulate matter are a complex physical change process, and the degree of damage to the health of construction workers is closely related to the exposure dose. Thus, suitable quantitative and evaluation methods need to be adopted. The current on-site particulate matter concentration control system lacks technical and data support and cannot support the needs of on-site environmental management. In this paper, three construction sites in different stages of construction in Shanghai were selected to measure the mass concentration of open source particulate matter, and on this basis, the emission factors of particulate matter in different operating areas were calculated. At foundation stage, the emission factor of TSP, PM10, PM2.5 are 0.0214 g/m2·h, 0.0067 g/m2·h, 0.0054 g/m2·h; at main structure stage, the emission factor of TSP, PM10, PM2.5 are 0.0136 g/m2·h, 0.0053 g/m2·h, 0.0041 g/m2·h; at installation and decoration stage, the emission factor of TSP, PM10, PM2.5 are 0.0165 g/m2·h, 0.0059 g/m2·h, 0.0043 g/m2·h. Using simulation software to simulate the temporal and spatial distribution of particulate matter concentration at the site of the example project, it is found that workers engaged in pit bottom operation in the foundation stage, steel bar processing in the main structure stage, and plastering, masonry and putty workers in the installation and decoration stage are the people with the highest occupational health risk at the construction site. In this study, DALYs were used as a metric to monetize the health risks of particulate matter to workers in the field. Support scientific decision-making on particulate matter control at construction sites and improve the level of on-site occupational health management.

Synergistic association of long-term ozone exposure and solid fuel use with biomarkers of advanced fibrosis

This study aims to explore the association of combined exposure to cooking fuel type and ambient ozone (O3) levels with hepatic fibrosis indices among rural adults. A total of 21,010 participants were derived from the Henan Rural Cohort. Information regarding cooking fuel type was collected through a questionnaire, and the concentration of ground-level O3 for each subject was obtained from the Tracking Air Pollution in China (TAP) dataset. A generalized linear model was used to examine the independent association of cooking fuel type or O3 exposure with hepatic fibrosis indices (FIB-4, APRI, and AST/ALT), and their possible interactions with advanced fibrosis were conducted. Compared to clean fuel users, solid fuel users had increased the risk of advanced fibrosis, the adjusted odds ratio (OR) of its assessment by FIB-4 1.240 (1.151, 1.336), by APRI 1.298 (1.185, 1.422), and by AST/ALT 1.135 (1.049, 1.227), respectively. Compared to low O3 exposure, the adjusted ORs of advanced fibrosis assessed by FIB-4, APRI, and AST/ALT in women with high O3 exposure were correspondingly 1.219 (1.138, 1.305), 1.110 (1.017, 1.212), and 0.883 (0.822, 0.949). The adjusted ORs of advanced fibrosis assessed by FIB-4, APRI, and AST/ALT for solid fuel users with high O3 exposure relative to clean fuel users with low O3 exposure in women were 1.557 (1.381, 1.755), 1.427 (1.237, 1.644), and 0.979 (0.863, 1.108), respectively. Significant additive effect of O3 exposure and solid fuel use on FIB-4-defined advanced fibrosis was observed in women, which was quantified by RERI (0.265, 95%CI: 0.052, 0.477), AP (0.170 95%CI: 0.045, 0.295), and SI (1.906, 95%CI: 1.058, 3.432). Solid fuel users with high O3 exposure were significantly associated with elevated hepatic fibrosis indices among rural women, suggesting that poor air quality may induce hepatocellular injury, and women might be more vulnerable to air pollution. The findings indicate that using cleaner fuels in cooking is an effective measure to maintain sustainable development of the environment and gain beneficial effect on human health. Clinical trial registration: The Henan Rural Cohort Study has been registered at the Chinese Clinical Trial Register (registration number: ChiCTR-OOC-15006699). Date of registration: 06 July 2015. http://www.chictr.org.cn/showproj.aspx?proj=11375.

Spatiotemporal variability and health risk assessment of PM2.5 and NO2 over the Indo-Gangetic Plain: A three years long study (2019-21)

Studying the spatiotemporal variability of pollutants is necessary to identify the pollution hotspots with high health risk and enable the agencies to implement pollution abatement strategies in a targeted manner. Present study reports the spatio-temporal variability and health risk assessment (HRA) of PM2.5 (Particulate matter with aerodynamic diameter <2.5μm) and NO2 over IGP from 2019-2021. The HRA is expressed as passively smoked cigarettes (PSC) for four different health outcomes i.e., low birth weight (LBW), percentage decreased lung function (DLF) in school aged children, lung cancer (LC), and cardiovascular mortality (CM). The findings confirm very high PM2.5 and NO2 mass concentrations and high health risk over middle IGP and Delhi as compared to upper and lower IGP. Within Delhi, north Delhi region is the most polluted and at highest risk as compared to central and south Delhi. The health risk associated with PM2.5 over IGP is highest for DLF, equivalent to 21.63 PSCs daily, followed by CM (11.69), LBW (8.27) and LC (6.94). For NO2, the health risk is highest for DLF (3.09 PSCs) and CM (2.95), followed by LC (1.47) and LBW (1.04). PM2.5 and NO2 concentrations, along with the associated health risks, are highest during the post-monsoon and winter seasons and lowest during the monsoon season.

COVID-19-associated 2020 lockdown: a study on atmospheric black carbon fall impact on human health

The mean mass concentrations of black carbon (BC)_, biomass burning (BC)_bb, and fossil fuel combustion (BC)_ff have been estimated during March-May 2020 (during the COVID-19 outbreak) and March-May 2019 at a semiarid region of Agra over the Indo-Gangetic basin region. The daily mean mass concentration of BC in 2020 and 2019 was 3.9 and 6.9 µg m^-3, respectively. The high monthly mean mass concentration of BC was found to be 4.7, 3.4 and 3.3 µg m^-3 in Mar-2020, Apr-2020, and May-2020, respectively, whereas in Mar-2019, Apr-2019, and May-2019 was 7.7, 7.5 and 5.4 µg m^-3, respectively. The absorption coefficient (b_abs) and absorption angstrom exponent (AAE) of black carbon were calculated. The highest mean AAE was 1.6 in the year 2020 (Mar-May 2020) indicating the dominance of biomass burning. The mean mass concentration of fossil fuel (BC)_ff and biomass burning (BC)_bb is 3.4 and 0.51 µg m^-3, respectively, in 2020 whereas 6.4 and 0.73 µg m^-3, respectively, in 2019. The mean fraction contribution of BC with fossil fuel (BC)_ff was 82.1 ± 13.5% and biomass burning (BC)_bb was 17.9 ± 4.3% in 2020, while in 2019, fossil fuel (BC)_ff was 86.7 ± 13.5% and biomass burning (BC)_bb was 13.3 ± 6.7%. The population-weighted mean concentration of BC, fossil fuel (BC)_ff, and biomass burning (BC)_bb has been calculated. The health risk assessment of BC has been analyzed in the form of attributable relative risk factors and attributed relative risk during the COVID-19 outbreak using AirQ + v.2.0 model. The attributable relative risk factors of BC were 20.6% in 2020 and 29.4% in 2019. The mean attributed relative risk per 10,000,000 populations at 95% confidence interval (CI) due to BC was 184.06 (142.6-225.2) in 2020 and 609.06 (418.3-714.6) in 2019. The low attributed factor and attributed relative risk in 2020 may be attributed to improvements in air quality and a fall in the emission of BC. In 2020, due to the COVID-19 pandemic, the whole country faced the biggest lockdown, ban of the transportation of private vehicles, trains, aircraft, and construction activities, and shut down of the industry leading to a fall in the impact of BC on human health. Overall, this was like a blessing in disguise. This study will help in future planning of mitigation and emission control of air pollutants in large and BC in particular. It only needs a multipronged approach. This study may be like torch bearing to set path for mitigation of impacts of air pollution and improvement of air quality.

Comparison of pollution status and source apportionment for PCBs and OCPs of indoor dust from an industrial city

In this study, the pollution status of polychlorinated biphenyls (PCBs) and organochlorine pesticides (OCPs) was investigated in indoor and outdoor dust from three different functional areas of Hefei, China. The relationship between the concentrations of PCBs and OCPs and different influencing factors in dwellings was studied. The results showed that the concentrations of PCBs and OCPs were higher in samples from dwellings with higher smoking frequency, lower cleaning frequency, higher floors and smaller household size. The results of Spearman's correlation coefficient analysis indicated that PCBs and OCPs were not consistently associated with each other, while sources of low-chlorinated PCBs and high-chlorinated PCBs were different. Scanning electron microscopy (SEM) shows the shape of indoor dust was a mixture of blocky, flocculated, spherical structures, and irregular shapes. The results of principal component analysis (PCA) and positive matrix factorization model (PMF) showed that the PCBs and OCPs of indoor dust came from both indoor and outdoor sources between local and regional transport. Carbon (δ¹³C) and Nitrogen (δ¹⁵N) stable isotope results indicate or show that the indoor dust (δ¹³C: - 24.37‰, δ¹⁵N: 6.88‰) and outdoor dust (δ¹³C: - 12.65‰, δ¹⁵N: 2.558‰) is derived from fossil fuel, coal combustion, road dust, fly ash, C4 biomass and soil. Potential source contribution factor (PSCF) and concentration weighted-trajectory analysis suggest that sources of pollutants were local and regional transport from surrounding provinces and marine emissions. The average daily dose (adult: 8.20E-04, children: 2.37E-03) of pollutants and the carcinogenic risks (adult: 1.23E-02, children: 2.65E-02) were relatively greater for children than adults. This study demonstrates the utility of SEM to characterize indoor dust morphology while combining PMF, PSCF, and stable isotope methods in identifying indoor PCBs and OCPs sources and regions.

Characteristics, seasonal variations, and dry deposition fluxes of carbonaceous and water-soluble organic components in atmospheric aerosols over China's marginal seas

A total of 37 atmospheric aerosol samples were collected over the Yellow and Bohai Seas (YBS) during four cruises in autumn, winter, spring and summer from 2017 to 2018. The concentrations of organic carbon (OC) and water-soluble organic carbon (WSOC) ranged from 1.04 to 15.43 μg m^-3 and 0.77-5.49 μg m^-3, respectively, with higher values in autumn and winter than in spring and summer. WSOC contributed 68.49 % to OC in summer and 34.55 % in winter and was affected by temperature and relative humidity. Dicarboxylic acid showed a predominance of oxalic acid followed by malonic and then succinic acids. The contributions of secondary sources to OC and WSOC were 54 % and 65.3 %, respectively, indicating the importance of secondary aging in improving the water solubility of OC. The dry deposition flux of WSOC over the YBS was estimated to be 0.87 mg m^-2 d^-1, which might play a potential role in the marine carbon cycle.

Occurrence and source of PAHs in Miankaleh International Wetland in Iran

We examined the occurrence and sources of 16 priority PAHs in the water and sediment samples of the Miankaleh Wetland (Coastal Biosphere Reserve), famous for harbouring huge flocks of migrating birds. The water and sediment samples collected from various locations were visualized and processed using a self-organizing map, positive matrix factorization and GIS. All the sediment samples, and >90% of the water samples, showed some degree of PAHs contamination. Higher PAH levels occur near the Chopoghi Channel, powerplants, sewage outfalls, and near fishing operations. Compared with previous study in this area, the PAHs concentration in the sediments of aquatic ecosystem of Miankaleh Wetland is increasing. The levels of PAH contamination seem too low to account for the mass deaths of migratory birds, and botulinus contamination seems the likely cause. Fugacity calculations show that the sediments act as a sink for PAHs. According to PMF and SOM analyses, three origins of PAHs were recognized: (i) fossil fuel and vehicular emissions with high-molecular weight PAHs (4-5 ring); (ii) municipal and industrial sewages characterized by low-molecular weight PAHs (2-3 ring) typical of petrogenic sources; and (iii) port activity characterized by prevalence of petrogenic influence and petroleum-related activities (combustion PAHs and low-molecular weight PAHs) consistent with port activity. This wetland needs serious attention because of continuous input of pollutants. The results and the methods used in this study may assist in improving coastal wetlands management.

Characteristics and source apportionment of PM2.5 under the dual influence of the Spring Festival and the COVID-19 pandemic in Yuncheng city

Based on the online and membrane sampling data of Yuncheng from January 1st to February 12th, 2020, the formation mechanism of haze under the dual influence of Spring Festival and COVID-19 (Corona Virus Disease) was analyzed. Atmospheric capacity, chemical composition, secondary transformation, source apportionment, backward trajectory, pollution space and enterprise distribution were studied. Low wind speed, high humidity and small atmospheric capacity inhibited the diffusion of air pollutants. Four severe pollution processes occurred during the period, and the pollution degree was the highest around the Spring Festival. In light, medium and heavy pollution periods, the proportion of SNA (SO₄^2-, NO₃^- and NH₄⁺) was 59.6%, 56.0% and 54.9%, respectively, which was the largest components of PM_2.5; the [NO₃^-]/[SO₄^2-] ratio was 2.1, 1.5 and 1.7, respectively, indicating that coal source had a great influence; the changes of NOR (nitrogen oxidation ratio, 0.44, 0.45, 0.61) and SOR (sulphur oxidation ratio, 0.40, 0.49, 0.65) indicated the accumulation of secondary aerosols with increasing pollution. The coal combustion, motor vehicle, secondary inorganic sources and industrial sources contributed 36.8%, 26.59%, 11.84% and 8.02% to PM_2.5 masses, respectively. Backward trajectory showed that the influence from the east was greater during the Spring Festival, and the pollutants from the eastern air mass were higher, which would aggravate the pollution. Meteorological and Spring Festival had a great impact on heavy pollution weather. Although some work could not operate due to the impact of the COVID-19 epidemic, the emission of pollutants did not reduce much.

PM2.5 Concentration Prediction Model: A CNN-RF Ensemble Framework

Although many machine learning methods have been widely used to predict PM2.5 concentrations, these single or hybrid methods still have some shortcomings. This study integrated the advantages of convolutional neural network (CNN) feature extraction and the regression ability of random forest (RF) to propose a novel CNN-RF ensemble framework for PM2.5 concentration modeling. The observational data from 13 monitoring stations in Kaohsiung in 2021 were selected for model training and testing. First, CNN was implemented to extract key meteorological and pollution data. Subsequently, the RF algorithm was employed to train the model with five input factors, namely the extracted features from the CNN and spatiotemporal factors, including the day of the year, the hour of the day, latitude, and longitude. Independent observations from two stations were used to evaluate the models. The findings demonstrated that the proposed CNN-RF model had better modeling capability compared with the independent CNN and RF models: the average improvements in root mean square error (RMSE) and mean absolute error (MAE) ranged from 8.10% to 11.11%, respectively. In addition, the proposed CNN-RF hybrid model has fewer excess residuals at thresholds of 10 μg/m³, 20 μg/m³, and 30 μg/m³. The results revealed that the proposed CNN-RF ensemble framework is a stable, reliable, and accurate method that can generate superior results compared with the single CNN and RF methods. The proposed method could be a valuable reference for readers and may inspire researchers to develop even more effective methods for air pollution modeling. This research has important implications for air pollution research, data analysis, model estimation, and machine learning.

Waste management beyond the COVID-19 pandemic: Bibliometric and text mining analyses

The outbreak of the COVID-19 pandemic has significantly increased the demand for personal protective equipment, in particular face masks, thus leading to a huge amount of healthcare waste generated worldwide. Consequently, such an unprecedented amount of newly emerged waste has posed significant challenges to practitioners, policy-makers, and municipal authorities involved in waste management (WM) systems. This research aims at mapping the COVID-19-related scientific production to date in the field of WM. In this vein, the performance indicators of the target literature were analyzed and discussed through conducting a bibliometric analysis. The conceptual structure of COVID-19-related WM research, including seven main research themes, were uncovered and visualized through a text mining analysis as follows: (1) household and food waste, (2) personnel safety and training for waste handling, (3) sustainability and circular economy, (4) personal protective equipment and plastic waste, (5) healthcare waste management practices, (6) wastewater management, and (7) COVID-19 transmission through infectious waste. Finally, a research agenda for WM practices and activities in the post-COVID-19 era was proposed, focusing on the following three identified research gaps: (i) developing a systemic framework to properly manage the pandemic crisis implications for WM practices as a whole, following a systems thinking approach, (ii) building a circular economy model encompassing all activities from the design stage to the implementation stage, and (iii) proposing incentives to effectively involve informal sectors and local capacity in decentralizing municipal waste management, with a specific focus on developing and less-developed countries.

COVID-19 and Sustainable Development Goals (SDGs): Scenario analysis through fuzzy cognitive map modeling

The COVID-19 crisis has immensely impacted the implementation of the 2030 Agenda for Sustainable Development worldwide. This research aims at providing a policy response to support achieving the Sustainable Development Goals (SDGs) taking the COVID-19 long-term implications into account. To do so, a qualitative analytical method was employed in the following four steps. First, a fuzzy cognitive map was developed to specify causal-effect links of the interdependent SDGs in Iran as a developing country in the Middle East. Second, potential effects of the pandemic on the SDGs achievement were analyzed. Third, five strategies were formulated, including green management, sustainable food systems, energizing the labor market, inclusive education, and supporting research and technology initiatives in the energy sector. And finally, different scenarios corresponding to the five proposed strategies were tested based on the identified interconnections among the SDGs. The analysis showed that applying each of the five considered strategies or their combination would mitigate the effect of COVID-19 on the SDGs only in case of a medium pandemic activation level. Moreover, implementing a single strategy with a high activation level leads to better outcomes on the SDGs rather than applying a combination of strategies in low or medium activation levels during the pandemic situation. The provided insights support stakeholders and policy-makers involved in the post-COVID-19 recovery action plan towards implementing the 2030 Agenda for Sustainable Development.

Association analysis between socioeconomic factors and urban ozone pollution in China

Ozone pollution in China has gradually increased, attracting extensive attention. Existing studies on ozone pollution typically take environmental and chemical perspectives. As air pollution is closely related to social and economic activities, it is also important to study ozone pollution from a socioeconomic perspective. Using the association rule mining technique, we uncovered hidden patterns between ozone variance and socioeconomic factors in macro-, meso-, and micro-scenarios in 297 Chinese cities. We found that the acceleration of urbanization and industrialization has indeed aggravated urban ozone pollution. The supply of water and power resources may be a significant factor influencing urban ozone pollution. Transportation hub cities with more developed economies and industries are more likely to suffer from ozone pollution in summer and autumn. Human behavior is a critical factor influencing the weekly variance in ozone concentration during weekdays and weekends. The influence of plant-derived VOC emissions on the formation of ozone cannot be overlooked. Our results deepen the understanding of ozone pollution in Chinese cities, and we provide corresponding policy recommendations to alleviate ozone pollution and improve air quality.

Improving performance of deep learning predictive models for COVID-19 by incorporating environmental parameters

The Coronavirus disease 2019 (COVID-19) pandemic has severely crippled the economy on a global scale. Effective and accurate forecasting models are essential for proper management and preparedness of the healthcare system and resources, eventually aiding in preventing the rapid spread of the disease. With the intention to provide better forecasting tools for the management of the pandemic, the current research work analyzes the effect of the inclusion of environmental parameters in the forecasting of daily COVID-19 cases. Three univariate variants of the long short-term memory (LSTM) model (basic/vanilla, stacked, and bi-directional) were employed for the prediction of daily cases in 9 cities across 3 countries with varying climatic zones (tropical, sub-tropical, and frigid), namely India (New Delhi and Nagpur), USA (Yuma and Los Angeles) and Sweden (Stockholm, Skane, Uppsala and Vastra Gotaland). The results were compared to a basic multivariate LSTM model with environmental parameters (temperature (T) and relative humidity (RH)) as additional inputs. Periods with no or minimal lockdown were chosen specifically in these cities to observe the uninhibited spread of COVID-19 and explore its dependence on daily environmental parameters. The multivariate LSTM model showed the best overall performance; the mean absolute percentage error (MAPE) showed an average of 64% improvement from other univariate models upon the inclusion of the above environmental parameters. Correlation with temperature was generally positive for the cold regions and negative for the warm regions. RH showed mixed correlations, most likely driven by its temperature dependence and effect of allied local factors. The results suggest that the inclusion of environmental parameters could significantly improve the performance of LSTMs for predicting daily cases of COVID-19, although other positive and negative confounding factors can affect the forecasting power.

Wildfire-induced pollution and its short-term impact on COVID-19 cases and mortality in California

Globally, wildfires have seen remarkable increase in duration and size and have become a health hazard. In addition to vegetation and habitat destruction, rapid release of smoke, dust and gaseous pollutants in the atmosphere contributes to its short and long-term detrimental effects. Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has emerged as a public health concern worldwide that primarily target lungs and respiratory tract, akin to air pollutants. Studies from our lab and others have demonstrated association between air pollution and COVID-19 infection and mortality rates. However, current knowledge on the impact of wildfire-mediated sudden outburst of air pollutants on COVID-19 is limited. In this study, we examined the association of air pollutants and COVID-19 during wildfires burned during August-October 2020 in California, United States. We observed an increase in the tropospheric pollutants including aerosols (particulate matter [PM]), carbon monoxide (CO) and nitrogen dioxide (NO2) by approximately 150%, 100% and 20%, respectively, in 2020 compared to the 2019. Except ozone (O3), similar proportion of increment was noticed during the peak wildfire period (August 16 - September 15, 2020) in the ground PM2.5, CO, and NO2 levels at Fresno, Los Angeles, Sacramento, San Diego and San Francisco, cities with largest active wildfire area. We identified three different spikes in the concentrations of PM2.5, and CO for the cities examined clearly suggesting wildfire-induced surge in air pollution. Fresno and Sacramento showed increment in the ground PM2.5, CO and NO2 levels, while San Diego recorded highest change rate in NO2 levels. Interestingly, we observed a similar pattern of higher COVID-19 cases and mortalities in the cities with adverse air pollution caused by wildfires. These findings provide a logical rationale to strategize public health policies for future impact of COVID-19 on humans residing in geographic locations susceptible to sudden increase in local air pollution.

Statistical Analysis for Understanding PM2.5 Air Quality and the Impacts of COVID-19 Social Distancing in Several Provinces and Cities in Vietnam

Air pollution, especially in urban regions, is receiving increasing attention in Vietnam. Consequently, this work aimed to study and analyze the air quality in several provinces and cities in the country focusing on PM_2.5. Moreover, the impacts of COVID-19 social distancing on the PM_2.5 level were investigated. For this purpose, descriptive statistic, Box and Whisker plot, correlation matrix, temporal variation, and trend analysis were conducted. R-based program and the R package "openair" were employed for the calculations. Hourly PM_2.5 data were obtained from 8 national air quality monitoring sites. The study results indicated that provinces and cities in the North experienced more PM_2.5 pollution compared to the Central and South. PM_2.5 concentrations at each monitoring site varied significantly. Among monitoring sites, the northern sites showed high PM_2.5 correlations with each other than the other sites. Seasonal variation was observed with high PM_2.5 concentration in the dry season and low PM_2.5 concentration in the wet season. PM_2.5 concentration variation during the week was not so different. Diurnal variation showed that PM_2.5 concentration rose at peak traffic hours and dropped in the afternoon. There was mainly a decreasing trend in PM_2.5 concentration over the studied period. The COVID-19 pandemic has contributed to PM_2.5 reduction. In the months implemented social distancing for preventing the epidemic, PM_2.5 concentration declined but it would mostly increase in the following months. This study provided updated and valuable assessments of recent PM_2.5 air quality in Vietnam.

Levels and drivers of urban black carbon and health risk assessment during pre- and COVID19 lockdown in Augsburg, Germany

This study aimed to evaluate the levels and phenomenology of equivalent black carbon (eBC) at the city center of Augsburg, Germany (01/2018 to 12/2020). Furthermore, the potential health risk of eBC based on equivalent numbers of passively smoked cigarettes (PSC) was also evaluated, with special emphasis on the impact caused by the COVID19 lockdown restriction measures. As it could be expected, peak concentrations of eBC were commonly recorded in morning (06:00-8:00 LT) and night (19:00-22:00 LT) in all seasons, coinciding with traffic rush hours and atmospheric stagnation. The variability of eBC was highly influenced by diurnal variations in traffic and meteorology (air temperature (T), mixing-layer height (MLH), wind speed (WS)) across days and seasons. Furthermore, a marked "weekend effect" was evidenced, with an average eBC decrease of ∼35% due to lower traffic flow. During the COVID19 lockdown period, an average ∼60% reduction of the traffic flow resulted in ∼30% eBC decrease, as the health risks of eBC exposure was markedly reduced during this period. The implementation of a multilinear regression analysis allowed to explain for 53% of the variability in measured eBC, indicating that the several factors (e.g., traffic and meteorology) may contribute simultaneously to this proportion. Overall, this study will provide valuable input to the policy makers to mitigate eBC pollutant and its adverse effect on environment and human health.

Historical trend and drivers of China's CO2 emissions from 2000 to 2020

China is the largest CO2 emitter in the world and announced that carbon peak and neutral targets will be achieved before 2030 and 2060, respectively. A retrospective analysis of past CO2 emissions and their drivers is important for the actions of peaking CO2 emissions before 2030 in China. CO2 emissions from energy use (coal, oil, and natural gas) and cement production from 2000 to 2020 were calculated first, and their drivers were decomposed into economic and population growth, energy intensity, and emission coefficient by logarithmic mean Divisa index (LMDI) analysis in this study. China's CO2 emissions increased nearly threefold from 3385 in 2000 to 10,788 million tonnes (Mt) in 2020, with a decline from 2013 to 2016. Coal was the major emission sector contributing more than 70% in most years, while natural gas emissions increased nearly 13 times from 53 to 723 Mt in the two decades, although its contribution only accounted for 6.7% in 2020. Economic growth was the major positive driver, while energy intensity reduction was the major negative driver of the emission increments by year and by the Five Year Plan (FYP). Emission coefficient reduction gradually became important due to its negative effect, especially in the 13th FYP, which offset ~ 30% of the emissions induced by economic growth. The projections of CO2 emissions in 2025, 2030, and 2035 could be 11,596 ± 582, 11,774 ± 621, and 11,401 ± 672 Mt, respectively, suggesting that China's carbon emissions could peak around 2030 with an increment of ~ 1000 Mt on the 2020 levels. Under the sustainable growth of the economy and population, it is possible to reduce the carbon peak value or achieve peak time earlier through the additional reduction of energy intensity and emission coefficient by technological progress and energy alternatives such as non-fossil fuels.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1007/s10668-022-02811-8.

Correlating the trends of COVID-19 spread and air quality during lockdowns in Tier-I and Tier-II cities of India-lessons learnt and futuristic strategies

The present study focuses on the impact of early imposed lockdowns and following unlocking phases on the status of air quality in six Tier-I and nine Tier-II cities of India as compared to the pre-lockdown measures. Furthermore, the study highlights the possible correlation of air quality index (AQI) with the initial trend of COVID-19 issues including the vaccination cases. Based on the statistical data analysis, we observed that the long-term averages for representing the short-term pre-lockdown conditions can impose a healing effect to the observed anomalies in air pollution data. However, the reduction in air pollution during the imposed lockdown series was only a phenomenal consequence, and the trends started reversing during the later phases of partial unlocking, where the correlation showed reversing trends. Being a yearly averaged parameter, the marginal reductions in the exceedance factor (EF) alone could not dictate air quality compared to the AQI. As there is incoherent variability in the pollutant distributions among the cities during various phases of the study, the trend analysis served as a preferable criterion to choose the preferred sources of variations. Based on the results, the correlation analysis revealed that air quality expressed in terms of AQI can act as an important precursor to estimate the critical phase of COVID-19 spread and the effectiveness of various control measures taken during each phase. Based on our proposed ranking, Kolkata and Patna are ranked first in the Tier-I and Tier-II cities respectively according to their responsiveness to the various institutionalized restrictions in terms of air quality parameters.

Centennial records of Polycyclic aromatic hydrocarbons and black carbon in Altay Mountains peatlands, Xinjiang, China

Black carbon (BC) is an important inert carbon component in the cycling process of surface carbon. Polycyclic aromatic hydrocarbons (PAHs) are a kind of thick cyclic organic compounds with carcinogenic, teratogenic, and mutagenic effects. The incomplete burning process of biomass and carbolic fuel is the important source of their co-occurrence. This study collected a 60-cm peat core from the Jiadengyu (JDY), Altay Mountain. The core was dated using the 210Pb and 137Cs methods. The results showed BC, total organic carbon, and PAHs of the JDY peat core to be 1.14–72.6 mg g−1, 17.09–47.2%, and 260.58–1,610.77 ng·g−1, respectively. δ13CBC was between −31.5 and − 29.4‰ (mean of −30.56‰). The results of scanning electron microscopy (SEM) indicated irregular or lumpy peat BC particles, retaining a plant fiber structure. δ13CBC, ratios of PAHs, and the SEM revealed that the BC to be the dominant source of biomass combustion in the peatland. BC showed an increasing trend between 1950 and 1980, after which it decreased. The discrepancy between the change in BC and δ13CBC from the national pattern of BC emission likely reflects the effect of local agricultural exploration, and thus an increase in crop burning.

Assessment of air quality before and during the COVID-19 and its potential health impacts in an arid oasis city: Urumqi, China

As a key node city of the "Silk Road Economic Belt" Urumqi has been listed as one of the ten most polluted cities in the world, posing a serious threat to the urban environment and residents' health. This study analyzed the air quality before and during the COVID-19 (Coronavirus disease 2019) pandemic and its potential health effects based on the data of PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃_8h levels from 10 air quality monitoring stations in Urumqi from January 1, 2017, to December 31, 2021. As per the results, the concentrations of the air pollutants PM_2.5, PM₁₀, SO₂, NO₂, CO, and O₃_8h in Urumqi from 2017 to 2021 showed a cyclical trend, and the implementation of COVID-19 prevention and control measures could effectively reduce the concentration(ρ) of air pollutants. The mean value of ρ(PM_2.5) decreased from 2017 to 2021, whereas ρ(O₃_8h) showed a waveform change trend (increased in 2017-2018, decreased in 2018-2020, and increased after 2020). Meanwhile, the maximum annual average values of ρ(PM_2.5) and ρ(O₃_8h) for the six monitoring stations during 2017-2021 occurred at sites S2 (74.37 µg m^-3) and S6 (91.80 µg m^-3), respectively; rapid industrialization had a greater impact on PM_2.5 and O₃_8h concentrations compared to commercial and residential areas. In addition, the air quality index data series can characterize the fluctuation trend of PM_2.5. The high pollution levels (Class IV and V) of the air pollutants PM_2.5 and O₃_8h in Urumqi have been decreasing annually, and good days can account for 80-95% of the total number of days in the year, indicating that the number of days with a potential threat to residents' health is gradually decreasing. Therefore, more attention should be paid in controlling and managing air pollution in Urumqi.

Lockdown during COVID-19 pandemic: A case study from Indian cities shows insignificant effects on persistent property of urban air quality

The influence of reduction in emissions on the inherent temporal characteristics of PM_2.5 and NO₂ concentration time series in six urban cities of India is assessed by computing the Hurst exponent using Detrended Fluctuation Analysis (DFA) during the lockdown period (March 24–April 20, 2020) and the corresponding period during the previous two years (i.e., 2018 and 2019). The analysis suggests the anticipated impact of confinement on the PM_2.5 and NO₂ concentration in urban cities, causing low concentrations. It is observed that the original PM_2.5 and NO₂ concentration time series is persistent but filtering the time series by fitting the autoregressive process of order 1 on the actual time series and subtracting it changes the persistence property significantly. It indicates the presence of linear correlations in the PM_2.5 and NO₂ concentrations. Hurst exponent of the PM_2.5 and NO₂ concentration during the lockdown period and previous two years shows that the inherent temporal characteristics of the short-term air pollutant concentrations (APCs) time series do not change even after withholding the emissions. The meteorological variations also do not change over the three time periods. The finding helps in developing the prediction models for future policy decisions to improve urban air quality across cities.

Biochar changed the distribution of imidacloprid in a plant-soil-groundwater system

The use of biochar has increased, as its physicochemical properties reduce the adverse effects of pesticides. However, few studies have comprehensively investigated the effects of biochar on the distribution of pesticides in a plant-soil-groundwater system. In this study, a biochar produced from rice straw at 550 °C was chosen, and column experiments with five rated of biochar application (application rates = 0.0, 1.0, 2.0, 3.0, and 4.0% w/w for B0-B4, respectively) were conducted to investigate the capacity of biochar to immobilize imidacloprid (IMI) in soil, thereby decreasing its uptake by plants and leaching from soil into groundwater. Our results showed that IMI in plants, leached from soil, and detected in soil accounted for 3.78, 1.76, and 36.4% of the total IMI input, respectively, and the biochar treatments dramatically decreased the IMI distribution to 0.57, 0.11, and 13.4%, respectively. By contrast, the percentage of undetected IMI increased from 58.1% in the B0 treatment to an average of 86.0% in the biochar treatments. Biochar treatments increased IMI immobilization in soil, which could be related to the increased soil carbon content, surface area, cation exchange capacity. This study indicates that biochar with characters of high surface area and porosity can stabilize IMI and reduce its potential to harm plants and groundwater.

Ecological risk assessment of priority PAHs pollutants in crude oil contaminated soil and its impacts on soil biological properties

Polycyclic aromatic hydrocarbons (PAHs) are one of the major toxic constituents of crude oil and therefore, an understanding on PAHs associated risks and their relationship with soil biological parameters are necessary for adopting effective risk-based and site specific remediation strategies in the contaminated soil. Here, risks evaluation of eight detected PAHs in terms of toxic equivalent concentration (TEQ_C), benzo(a)pyrene equivalent (BaPeq), contamination factor (CF), pollution load index (PLI), hazard quotient (HQ), hazard index (HI), toxic unit for individual PAHs (TU) and PAHs mixture (TU_m) have been evaluated. Besides, the effect of PAHs contamination on soil biological properties has also been investigated and correlated with PAHs concentrations. The TEQc of eight PAHs was recorded in the range of 0.06-5.0 mg kg^-1 soil_, whereas the BaPeq value was 25.3 mg kg^-1 soil which exceed the permissible limit. Similarly, CF (85.5-1668.2), PLI (322.8), HQ (311.7-8340.9), HI (26,443.8), TU (227.9-3821.6) and TU_m(7916.2) also exceed the permissible values for non-toxic conditions indicating carcinogenic risk for humans. Besides, activities of soil dehydrogenase, urease, alkaline-phosphatase, catalase, amylase and cellulase were decreased by 1.5-2.3 folds in the contaminated soil than control. The results of Pearson's correlation matrix also established negative impact of PAHs on the soil's biological properties.

Temporary reduction in VOCs associated with health risk during and after COVID-19 in Maharashtra, India

A novel coronavirus has affected almost all countries and impacted the economy, environment, and social life. The short-term impact on the environment and human health needs attention to correlate the Volatile organic compounds (VOCs) and health assessment for pre-, during, and post lockdowns. Therefore, the current study demonstrates VOC changes and their effect on air quality during the lockdown. The findings of result, the levels of the mean for total VOC concentrations were found to be 15.45 ± 21.07, 2.48 ± 1.61, 19.25 ± 28.91 µg/m3 for all monitoring stations for pre-, during, and post lockdown periods. The highest value of TVOCs was observed at Thane, considered an industrial region (petroleum refinery), and the lowest at Bandra, which was considered a residential region, respectively. The VOC levels drastically decreased by 52%, 89%, 80%, and 97% for benzene, toluene, ethylbenzene, and m-xylene, respectively, during the lockdown period compared to the previous year. In the present study, the T/B ratio was found lower in the lockdown period as compared to the pre-lockdown period. This can be attributed to the complete closure of non-traffic sources such as industries and factories during the lockdown. The Lifetime Cancer Risk values for all monitoring stations for benzene for pre-and-post lockdown periods were higher than the prescribed value, except during the lockdown period.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10874-022-09440-5.

Decrease of atmospheric black carbon and CO2 concentrations due to COVID-19 lockdown at the Mt. Waliguan WMO/GAW baseline station in China

The Coronavirus Disease 2019 (COVID-19) lockdown policy reduced anthropogenic emissions and impacted the atmospheric chemical characteristics in Chinese urban cities. However, rare studies were conducted at the high mountain site. In this work, in-situ measurements of light absorption by carbonaceous aerosols and carbon dioxide (CO₂) concentrations were conducted at Waliguan (WLG) over the northeastern Tibetan Plateau of China from January 3 to March 30, 2020. The data was employed to explore the influence of the COVID-19 lockdown on atmospheric chemistry in the background-free troposphere. During the sampling period, the light absorption near-infrared (>470 nm) was mainly contributed by BC (>72%), however, BC and brown carbon (BrC) contributed equally to light absorption in the short wavelength (∼350 nm). The average BC concentrations in the pre-, during and post-lockdown were 0.28 ± 0.25, 0.18 ± 0.16, and 0.28 ± 0.20 μg m^-3, respectively, which decreased by approximately 35% during the lockdown period. Meanwhile, CO₂ also showed slight decreases during the lockdown period. The declined BC was profoundly attributed to the reduced emissions (∼86%), especially for the combustion of fossil fuels. Moreover, the declined light absorption of BC, primary and secondary BrC decreased the solar energy absorbance by 35, 15, and 14%, respectively. The concentration weighted trajectories (CWT) analysis suggested that the decreased BC and CO₂ at WLG were exclusively associated with the emission reduction in the eastern region of WLG. Our results highlighted that the reduced anthropogenic emissions attributed to the lockdown in the urban cities did impact the atmospheric chemistry in the free troposphere of the Tibetan Plateau.

COVID-19 induced restriction in developing countries and its impacts on pollution load: case study of Lagos mega city

Sudden outbreak of COVID-19 pandemic globally in 2020 warranted urgent course of actions to guide against its escalation. The first and immediate measure adopted by several nations was the imposition of restriction on transport, industrial, commercial and social activities; and this step has thus, provided a platform for the impact assessment of the restrictions on ambient air quality, especially in developing nations such as Nigeria. The levels of four criteria air pollutants (PM_2.5, SO₂, NO₂, and PM₁₀) in ambient air of Lagos city before, during and after the restriction periods were compared to establish the extent of change caused by the restrictions. The results revealed a decline of 74.0, 79.7, 55.0 and 58.5% in the levels of SO₂, NO₂, PM_2.5, and PM₁₀, respectively during the lockdown period. The results also revealed that, despite the huge reduction in the atmospheric emissions witnessed during lockdown period, air quality within the region was still poor, as the levels of most of the pollutants were above the recommended limits. These findings suggested that apart from the restricted activities, there are other air pollution sources within the city which increased the pollution load in the ambient air. Conclusively, while the restriction led to untold economic hardship, it equally enhanced quality of ambient air. Cleaner technology is advocated to ensure reduction in the consumption of fossil fuel instead of the common practice of end-of-pipe technology, for environmental sustainability.

Association between Acute Exposure to PM2.5 Chemical Species and Mortality in Megacity Delhi, India

The association between daily all-cause mortality and short-term fine particulate matter (PM_2.5) exposure is well established in the literature. However, association between acute exposure to PM_2.5 chemical species and mortality is not well known, especially in developing countries like India. Here we examined associations between mortality and acute exposure to PM_2.5 mass concentration and their 15 chemical components using data from 2013 to 2016 in megacity Delhi using a semiparametric quasi-Poisson regression model, adjusting for mean temperature, relative humidity, and long-term time trend as the major potential confounders. Mortality estimates were further checked for effect modification by sex, age group, and season. The subspecies of NO₃^-, NH₄NO₃, Cr, NH₄⁺, EC, and OC showed a higher mortality impact than the total PM_2.5 mass. Males were at higher risk from NO₃^-, SO₄^2-, and their NH₄⁺ compounds along with carcinogen Cr, whereas female group was at higher risk from EC and OC. Among all age groups, the elderly above 65 years were the most vulnerable group prone to mortality effects from maximum species. The major mortality risk from all hazardous species arose from their winter exposures. Our study provides the first evidence of association between acute exposure to PM_2.5 chemical species and mortality anywhere in India and recommends similar studies in other regions so that sectoral mitigation emitting the most toxic species can be prioritized to maximize the health benefits.

Modeling of the air temperature using the Extreme Value Theory for selected biomes in Mato Grosso do Sul (Brazil)

This paper aims to find probabilities of extreme values of the air temperature for the Cerrado, Pantanal and Atlantic Forest biomes in Mato Grosso do Sul in Brazil. In this case a maximum likelihood estimation was employed for the probability distributions fitting the extreme monthly air temperatures for 2007-2018. Using the Extreme Value Theory approach this work estimates three probability distributions: the Generalized Distribution of Extreme Values (GEV), the Gumbel (GUM) and the Log-Normal (LN). The Kolmogorov-Smirnov test, the corrected Akaike criterion AIC c , the Bayesian information criterion BIC, the root of the mean square error RMSE and the determination coefficient R 2 were applied to measure the goodness-of-fit. The estimated distributions were used to calculate the probabilities of occurrence of maximum monthly air temperatures over 28-32 °C. Temperature predictions were done for the 2-, 5-, 10-, 30-, 50- and 100-year return periods. The GEV and GUM distributions are recommended to be used in the warmer months. In the coldest months, the LN distribution gave a better fit to a series of extreme air temperatures. Deforestation, combustion and extensive fires, and the related aerosol emissions contribute, alongside climate change, to the generation of extreme air temperatures in the studied biomes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00477-022-02206-1.

Traffic restrictions during COVID-19 lockdown improve air quality and reduce metal biodeposition in tree leaves

The coronavirus disease (COVID-19) has had a great global impact on human health, the life of people, and economies all over the world. However, in general, COVID-19´s effect on air quality has been positive due to the restrictions on social and economic activity. This study aimed to assess the impact on air quality and metal deposition of actions taken to reduce mobility in 2020 in two different urban locations. For this purpose, we analysed air pollution (NO2, NO, NOx, SO2, CO, PM10, O3) and metal accumulation in leaves of Tilia cordata collected from April to September 2020 in two cities in northern Spain (Pamplona-PA and San Sebastián-SS). We compared their values with data from the previous year (2019) (in which there were no mobility restrictions) obtained under an identical experimental design. We found that metal accumulation was mostly lower during 2020 (compared with 2019), and lockdown caused significant reductions in urban air pollution. Nitrogen oxides decreased by 33%-44%, CO by 24%-38%, and PM10 by 16%-24%. The contents of traffic-related metals were significantly reduced in both studied cities. More specifically, significant decreases in metals related to tyre and brake wear (Zn, Fe, and Cu) and road dust resuspension (Al, Ti, Fe, Mn, and Ca) were observed. With these results, we conclude that the main reason for the improvement in urban air pollutants and metals was the reduction in the use of cars due to COVID-19 lockdown. In addition, we offer some evidence indicating the suitability of T. cordata leaves as a tool for biomonitoring metal accumulation. This information is relevant for future use by the scientific community and policy makers to implement measures to reduce traffic air pollution in urban areas and to improve environmental and human health.

Assessment of the coronavirus disease 2019 (COVID-19) pandemic imposed lockdown and unlock effects on black carbon aerosol, its source apportionment, and aerosol radiative forcing over an urban city in India

A nationwide lockdown was imposed in India due to the Coronavirus Disease 2019 (COVID-19) pandemic which significantly reduced the anthropogenic emissions. We examined the characteristics of equivalent black carbon (eBC) mass concentration and its source apportionment using a multiwavelength aethalometer over an urban site (Ahmedabad) in India during the pandemic induced lockdown period of year 2020. For the first time, we estimate the changes in BC, its contribution from fossil (eBC ff ) and wood (eBC wf ) fuels during lockdown (LD) and unlock (UL) periods in 2020 with respect to 2017 to 2019 (normal period). The eBC mass concentration continuously decreased throughout lockdown periods (LD1 to LD4) due to enforced and stringent restrictions which substantially reduced the anthropogenic emissions. The eBC mass concentration increased gradually during unlock phases (UL1 to UL7) due to the phase wise relaxations after lockdown. During lockdown period eBC mass concentration decreased by 35%, whereas during the unlock period eBC decreased by 30% as compared to normal period. The eBC wf concentrations were higher by 40% during lockdown period than normal period due to significant increase in the biomass burning emissions from the several community kitchens which were operational in the city during the lockdown period. The average contributions of eBC ff and eBC wf to total eBC mass concentrations were 70% and 30% respectively during lockdown (LD1 to LD4) period, whereas these values were 87% and 13% respectively during the normal period. The reductions in BC concentrations were commensurate with the reductions in emissions from transportation and industrial activities. The aerosol radiative forcing reduced significantly due to the reduction in anthropogenic emissions associated with COVID-19 pandemic induced lockdown leading to a cooling of the atmosphere. The findings in the present study on eBC obtained during the unprecedented COVID-19 induced lockdown can provide a comprehensive understanding of the BC sources and current emission control strategies, and thus can serve as baseline anthropogenic emissions scenario for future emission control strategies aimed to improve air quality and climate.

Spatio-temporal modeling of COVID-19 prevalence and mortality using artificial neural network algorithms

The outbreak of coronavirus disease (COVID-19) has become one of the most challenging global concerns in recent years. Due to inadequate worldwide studies on spatio-temporal modeling of COVID-19, this research aims to examine the relative significance of potential explanatory variables (n = 75) concerning COVID-19 prevalence and mortality using multilayer perceptron artificial neural network topology. We utilized ten variable importance analysis methods to identify the relative importance of the explanatory variables. The main findings indicated that several variables were persistently among the most influential variables in all periods. Regarding COVID-19 prevalence, unemployment and population density were among the most influential variables with the highest importance scores. While for COVID-19 mortality, health-related variables such as diabetes prevalence and number of hospital beds were among the most significant variables. The obtained findings from this study might provide general insights for public health policymakers to monitor the spread of disease and support decision-making.

Analysis of the health, economic and environmental impacts of COVID-19: The Bangladesh perspective

Although COVID-19 has given an opportunity to the earth to restore her ecosystem, its role in bringing changes in every sector including social, economic, agricultural, industrial, education and health is enormous. The study was conducted to assess the socio-economic impacts of COVID-19 in Bangladesh by collecting data from different sources. The result depicted that during the first wave of COVID-19, the detection rate was less than 5%, exceeding almost 30% after detecting the deadlier Indian variant where 65% of the death is noticed by the people older than 50 years. Among all the frontline service providers during Covid, the highest rate of death was observed for doctors in Bangladesh. This study also discussed the impact of COVID-19 on mental health and found that women faced more depression and anxiety than men as well as 43% of children had subthreshold mental disturbances. Three-fourths of the adolescents have been distressed with household stress during the pandemic. Women and girls have encountered increased domestic violence whereas early marriages dropped out many rural girls from education. Decreasing remittance from non-residents and shutting down of RMG industry resulted loss of job and have badly affected economic section. Almost 20 million workers lost their jobs in Bangladesh from the informal sector. Moreover, the healthcare workers who have treated the corona virus patients have been socially stigmatized due to the fear of infection. Corona Virus has jeopardized the agriculture sector and 66% farmers (53% crop and vegetables, 99% fish farmers) got lower price than they used to get in a normal situation. Together with Government, non-government organizations, researchers, doctors, industrialists, international organization as well as individuals should come forward to handle this pandemic.

The influence of meteorological variables and lockdowns on COVID-19 cases in urban agglomerations of Indian cities

Coronavirus has been identified as one of the deadliest diseases and the WHO has declared it a pandemic and a global health crisis. It has become a massive challenge for humanity. India is also facing its fierceness as it is highly infectious and mutating at a rapid rate. To control its spread, many interventions have been applied in India since the first reported case on January 30, 2020. Several studies have been conducted to assess the impact of climatic and weather conditions on its spread in the last one and half years span. As it is a well-established fact that temperature and humidity could trigger the onset of diseases such as influenza and respiratory disorders, the relationship of meteorological variables with the number of COVID-19 confirmed cases has been anticipated. The association of several meteorological variables has therefore been studied in the past with the number of COVID-19 confirmed cases. The conclusions in those studies are based on the data obtained at an early stage, and the inferences drawn based on those short time series studies may not be valid over a longer period. This study attempted to assess the influence of temperature, humidity, wind speed, dew point, previous day's number of deaths, and government interventions on the number of COVID-19 confirmed cases in 18 districts of India. It is also attempted to identify the important predictors of the number of confirmed COVID-19 cases in those districts. The random forest model and the hybrid model obtained by modelling the random forest model's residuals are used to predict the response variable. It is observed that meteorological variables are useful only to some extent when used with the data on the number of the previous day's deaths and lockdown information in predicting the number of COVID-19 cases. Partial lockdown is more important than complete or no lockdown in predicting the number of confirmed COVID-19 cases. Since the time span of the data in the study is reasonably large, the information is useful to policymakers in balancing the restriction activities and economic losses to individuals and the government.

Variation and dispersal of PM10 and PM2.5 during COVID-19 lockdown over Kolkata metropolitan city, India investigated through HYSPLIT model

The higher concentration of PM10 and PM2.5 in the lower atmosphere is severely harmful for human health and it also makes visibility diminution along with weather and climate modifications. The main objective is to find out the spatiotemporal variation and dispersal of PM10 and PM2.5 along with COVID-19 infection in the dusty city Kolkata. The consecutive two years PM10 and PM2.5 data of different stations have been obtained from State Pollution Control Board, Govt. of West Bengal. Forward trajectory analysis has been done through HYSPLIT (Hybrid Single Particle Lagrangian Integrated Trajectory) model to find the path and direction of air particles. The result showed that the various meteorological or environmental factors (such as temperature, relative humidity, wind, wind speed, pressure and gusty wind) and geographical location regulate the spatiotemporal variation of PM10 and PM2.5. These factors like high temperature with relative humidity and strong wind influence to disperse the particulate matters from north to south direction from city to outside during summer in Kolkata metropolitan city. During summer (both pre and lockdown years), the height of particles is extended up to 1000 m owing to active atmospheric ventilation whereas in winter it is confined within 100 m. The HYSPLIT model clearly specified that the particles dispersed from south, south-west to north and north east direction due to strong wind. The constant magnification of PM10 and PM2.5 in the lower atmosphere leads to greater frequency of COVID-19 infections and deaths. In Kolkata, the one of the crucial reasons of high infection and deaths (COVID-19) is co-morbidity of people.

Effect on Ambient Air Quality in Nagpur due to lockdown to contain the spread of COVID-19 pandemic in the year 2020: a case study

The present paper deals with the studies on the change in concentration of three standard pollutants namely, respiratory suspended particulate matter (RSPM or PM10), Sulphur dioxide (SO2) and Nitrogen dioxide (NO2) because of lockdown in India to prevent the spread of COVID-19 pandemic in 2020. The monthly average concentrations of the above pollutants observed at four monitoring stations in and around Nagpur city during January to December 2020 were analyzed and compared. Due to COVID-19 pandemic, there was a complete lockdown from 25th March to 31st May 2020 and phased reopening of areas outside containment zones from June 1st onwards. It is found that the average concentration of all the three pollutants at all four stations was reduced by about 50 % to 75 % due to lockdown. During lockdown, the minimum concentration of PM10, SO2 and NO2 amongst all stations were found to be 40, 5 and 11 µg/m3, respectively, whereas the maximum concentrations were found to be 159, 20, and 50 µg/m3, respectively. The concentrations during lockdown were below the standards prescribed by CPCB, which were found to increase due to reopening. The Air quality index (AQI) at all four stations during lockdown was less than 50 (i.e. SATISFACTORY), whereas it increased above 100 (i.e. MODERATE) after reopening. As a result, the annual average concentration of pollutants was reduced in 2020 compared to previous years.

Traffic-induced atmospheric pollution during the COVID-19 lockdown: Dispersion modeling based on traffic flow monitoring in Turin, Italy

The COVID-19 pandemic, as a worldwide threat to public health, has led many governments to impose mobility restrictions and adopt partial or full lockdown strategies in many regions to control the disease outbreak. Although these lockdowns are imposed to save public health by reducing the transmission of the virus, rather significant improvements of the air quality in this period have been reported in different areas, mainly as a result of the reduction in vehicular trips. In this research, the city of Turin in the northern part of Italy has been considered as the study area, because of its special meteorology and geographic location in one of the most polluted regions in Europe, and also its high density of vehicular emissions. A Lagrangian approach is applied to illustrate and analyze the effect of imposing full lockdown restrictions on the reduction of traffic-induced air pollution in the city. To do this, the real-time traffic flow during the lockdown period is recorded, and by utilizing CALPUFF version 7, the dispersion of PM_2.5, Total Suspended Particulate (TSP), Benzo(a)pyrene (BaP), NO_x, and Black Carbon (BC) emitted from all circulating vehicles during and before the lockdown period are compared. Results indicate that the concentration of pollutants generated by road traffic sources (including passenger cars, busses, heavy-duty vehicles, light-duty vehicles, mopeds, and motorcycles) reduced at least 70% (for PM_2.5) up to 88.1% (for BaP) during the studied period. Concentration maps show that the concentration reduction varied in different areas of the town, mainly due to the characteristics and strength of the emission sources and the geophysical features of the area.

Declining carbon emission/concentration during COVID-19: A critical review on temporary relief

In December 2019 the deadly pandemic COVID-19 traumatized mankind through its lethal impact. To seize the outbreak, nationwide/region-based lockdown strategies were adopted by most of the COVID-19 affected countries. This in turn resulted in restricted transportation via surface, water, and air, as well as significantly reduced working hours of the industry sectors, so on and so forth. The obvious outcome was a sudden discernible decline in atmospheric adulteration. Accordingly, the anthropogenic emissions at the global and regional/local scales were examined during the lockdown period by several researchers using both or either satellite-based and ground-based monitoring. Among several other air-contaminants, carbon has a dominant toxicological profile causing adverse health effects and thereby attracting researches interest in carbon-release probing during the systematic confinement period imposed by the ruling authorities across the globe. The results of those studies indicated a confirmed decline in carbon emission/concentration making the air more breathable for the period. In this review, the studies related to anthropogenic emissions of carbon during the lockdown period are accounted for by compiling the recently reported data from published articles.