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Road deposited dust (RDD) is an important indicators of heavy metal contamination in urban areas. In this study, we measured eight heavy metals (V, Cr, Mn, Fe, Ni, Cu, Zn, and Pb) in RDD collected from 34 different locations in Jammu city represented by different land uses such as industrial, urban-residential, high-density traffic, and sub-urban locations, and evaluated their ecological and health risks. The ratio of metal concentrations in RDD to their respective background levels varied as: Cu (3.94) > Pb (3.75) > Zn (3.01) > Cr (1.75) > Ni (1.51) > Mn (1.40) > V (1.35) > Fe (1.1) suggesting Cr, Ni, Cu, Zn and Pb were enriched anthropogenically. Geospatial maps revealed a heterogeneous distribution of metals in Jammu city with metal(s) specific hotspots primarily localized around high traffic density locations and industrial clusters. The index of geoaccumulation indicated 32%, 26%, 20%, 9%, and 8%, of samples belonged to "moderately polluted" category for Zn, Cu, Pb, Cr, and Ni respectively. Health index (HI) showed low non-carcinogenic hazards of metal contamination to adults but a high hazard to children. Though the values of total carcinogenic risks (TCR) (6.53E-05 to 3.71E-04) considerably exceeded the USEPA acceptable levels (1 × 10 -6  ≤ TCR <1 × 10 -4 ) suggesting high carcinogenic risks of metal contamination to both adults and children. Besides potential ecological risk index (PERI) revealed that 56% of samples had PERI >40 suggesting "moderate to high ecological risk" of metal contamination in the Jammu city RDD., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Wearing a respirator is generally the most convenient individual intervention against ambient particulate matter (PM), and therefore there has been considerable research into its effectiveness. However, the effects of respirator intervention under different PM concentration settings have been insufficiently elucidated. We conducted a randomized, blinded, crossover intervention study in four representative cities in China in which 128 healthy university students spent 2-h walking along a busy road wearing either a real or a sham respirator and then spent the next 5-h indoors away from traffic pollution. Lung function, blood pressure, and heart rate variability were continuously measured throughout the visit. Linear mixed-effect models were fitted to evaluate the protective effects of respirator intervention on the cardiopulmonary indicators. Results showed that the beneficial effects of respirator intervention were only occasionally significant at specific time points or in specific cities or in selected parameters. Overall, respirator intervention was associated with reduced SBP (6.2 vs. 11.5 mmHg compared to baseline, p < 0.05) and increased LF (44 vs. 35 ms 2 compared to baseline, p < 0.05) over the 2-h walk, but no significant effects were found over the 7-h period. Respirators have significant effect modifications on the associations between PM 2.5 /PM 10 and the cardiopulmonary indicators, but the directions of effects were inconsistent. The intercity difference in the effects of respirator intervention was found significant, with Taiyuan and Shanghai to be the two cities with lower personal PM concentrations but more pronounced benefits. In conclusion, reducing personal exposure to PM can have some beneficial effects in some scenarios. However, respirators may not provide sufficient protection from air pollution overall, and we should avoid over-reliance on respirators and accelerate efforts to reduce emissions of pollutants in the first place. Despite standardized procedures, we found inconsistency in results across cities, consistent with the previous literature., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

The AOD derived from the MODIS deep blue(DB) algorithm and AQI were used to investigate the correlation between AOD and AQI in seven major cities of Yangtze River Delta (YRD) from January to December 2019. The accuracy of MODIS AOD was validated by AERONET. Moreover, the AOD and AQI were studied to explore the annual and seasonal distribution characteristics, and the correlation analysis was carried out using five regression models. It was found: Ⅰ) There was a significant correlation between AOD and AERONET data (R 2 ˃ 0.80, RMSE = 0.106, and MAE = 0.089). Ⅱ) The highest AQI was observed in winter (83), followed by spring (76), autumn (74), and summer (72). Ⅲ) The monthly average AOD showed noticeable seasonal variations, which reached the highest in summer (0.91) and the lowest in winter (0.69), followed by spring and autumn. Ⅳ) Among the five models, the cubic model obtained the best results with R 2 ˃ 0.55. In the sub-seasonal regression model, the cubic model outperformed other models in spring (R 2 ˃ 0.57), summer (R 2 ˃ 0.76) and autumn (R 2 ˃ 0.38). However, in winter the composite model outperformed others (R 2 ˃ 0.68). Ⅴ) Considering annual data, the AOD can predict over 70% of the variations in AQI (0.41＜R 2 ＜0.81). These results demonstrate the feasibility of AOD derived from the MODIS DB algorithm in AQI prediction. The method used in this study can be applied as an aid for air pollution control programs in different regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Characterising the daily PM2.5 concentration is crucial for air quality control. To govern the status of the atmospheric environment, a novel hybrid model for PM2.5 forecasting was proposed by introducing a two-stage decomposition technology of complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and variational mode decomposition (VMD); subsequently, a deep learning approach of long short-term memory (LSTM) was proposed. Five cities with unique meteorological and economic characteristics were selected to assess the predictive ability of the proposed model. The results revealed that PM2.5 pollution was generally more severe in inland cities (66.98 ± 0.76 μg m -3 ) than in coastal cities (40.46 ± 0.40 μg m-3). The modelling comparison showed that in each city, the secondary decomposition algorithm improved the accuracy and prediction stability of the prediction models. When compared with other prediction models, LSTM effectively extracted featured information and achieved relatively accurate time-series prediction. The hybrid model of CEEMDAN-VMD-LSTM achieved a better prediction in the five cities (R2 = 0.9803 ± 0.01) compared with the benchmark models (R2 = 0.7537 ± 0.03). The results indicate that the proposed approach can identify the inherent correlations and patterns among complex datasets, particularly in time-series analysis., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Heavy metal pollution in urban soils and dust is mostly caused by extensive anthropogenic activity during urbanization and industrialization. In this research study, the pollution characteristics, sources, ecological and human health risks of heavy metals in urban soil, and dust have been thoroughly evaluated. The research findings demonstrate that dust has a higher level of contamination than urban soil, such as Pb, Cu, and Zn metals are more contaminated in both urban soil and dust throughout the city, and Hg and As are also found in locations with a high concentration of heavy industrial companies. This implies that traffic emissions are still a significant source of metals in urban areas, though industrial companies also contribute. The health risk assessment model used to calculate human exposure revealed that the non-carcinogenic and carcinogenic risks of selected metals in soil and dust were generally in the low range, except for the carcinogenic risk from Cr in children. Statistical analysis revealed that Cr and Ni concentrations were mainly of natural origin, Cu and Zn have been sourced from traffic, whereas Pb, Hg, and As have been sourced from industrial activities. The overall recommendation is that the road traffic environment and municipal construction facilities need to be improved to ensure the sustainable development of the city's environment, while pollution from industrial waste is strongly controlled., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Exposure to potentially toxic trace elements (PTTEs) in inhalable particulate matter (PM 10 ) is associated with an increased risk of developing cardiorespiratory diseases. Therefore, in multi-source polluted urban contexts, a spatially-resolved evaluation of health risks associated with exposure to PTTEs in PM is essential to identify critical risk areas. In this study, a very-low volume device for high spatial resolution sampling and analysis of PM 10 was employed in Terni (Central Italy) in a wide and dense network (23 sampling sites, about 1 km between each other) during a 15-month monitoring campaign. The soluble and insoluble fraction of 33 elements in PM 10 was analysed through a chemical fractionation procedure that increased the selectivity of the elements as source tracers. Total carcinogenic risk (CR) and non-carcinogenic risk (NCR) for adults and children due to concentrations of PTTEs in PM 10 were calculated and quantitative source-specific risk apportionment was carried out by applying Positive Matrix Factorization (PMF) to the spatially-resolved concentrations of the chemically fractionated elements. PMF analysis identified 5 factors: steel plant, biomass burning, brake dust, soil dust and road dust. Steel plant showed the greatest risk contribution. Total CR and NCR, and source-specific risk contributions at the 23 sites were interpolated using the ordinary kriging (OK) method and mapped to geo-reference the health risks of the identified sources in the whole study area. This also allowed risk estimation in areas not directly measured and the assessment of the risk contribution of individual sources at each point of the study area. This innovative experimental approach is an effective tool to localize the health risks of spatially disaggregated sources of PTTEs and it may allow for better planning of control strategies and mitigation measures to reduce airborne pollutant concentrations in urban settings polluted by multiple sources., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Coal is the most extensively used fossil fuel in China. It is well documented that coal combustion detrimentally affected air quality, yet the contribution of coal mining activity to air pollution is still largely unknown. Homing pigeons have been applied to assess the occurrence of atmospheric pollutants within cities. Herein, we sampled homing pigeons from both urban and mining areas in a typical coal industry city (Datong, China) as biomonitors for assessing local air pollution. Target organic contaminants, including polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and organochlorine pesticides (OCPs) were frequently detected in lung, liver, and fat tissues of the pigeons. The pollutants were predominately accumulated in lung, validating that respiration was the main accumulation route for these compounds in homing pigeons. In addition, pathological damage examination in lung and liver tissues revealed that the exposure to atmospheric pollutants impaired pigeon health. While the concentrations of PCBs and OCPs were similar in pigeons from urban and mining areas, the concentrations of PAHs were higher in pigeons from urban area. In contrast, more elevated levels of PBDEs (particularly BDE-209) were found in the mining area, which was consistent with the greater pathological damages and particulate matter levels. Unlike coal combustion, coal mining activities did not increase atmospheric PAH exposure to homing pigeons, but intensified PBDE contamination along with increasing emission of particulate matters. Overall, homing pigeons are promising biomonitors for assessing the respiratory exposure and risk of atmospheric pollutants within cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

In this study, the human health risks of potentially harmful elements (PHEs) in urban surface deposited sediments (USDS) were examined by collecting urban dust samples, measuring their PHE concentrations, and using index evaluation. About 35, 12 and 16 samples are represented a green zones, roads, driveways and sidewalks in residential areas of Ekaterinburg, respectively. The dust fraction (0.002-0.1 mm) was obtained by sieving, filtration, and decantation process. Total concentrations of 10 PHEs were measured using inductively coupled plasma mass spectrometry. The highest concentrations of Pb were found in USDS from green zones, while Fe, V, Mn, Co, Ni, Sn, and Sb on roads, Cu and Zn on driveways and sidewalks. The contamination levels in the investigated land-use areas were studied, where the highest contamination was contributed from Sb in the driveways and sidewalk. Moreover, the pollution in the studied zones was a high load, contributing to anthropogenic activities and traffic emissions. No non-cancerogenic risk was attributed from the PHEs based on the results of health indices (HI < 1) for both adult and children, except Co and Ni which has HI > 1 for children. The total carcinogenic risk (TLCR) in all urban landscape areas is defined as a high potential inhalation exposure and a low potential ingestion and dermal exposure., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

It is crucial to precisely estimate the municipal solid waste (MSW) amount for its sustainable management. Owing to learning complicated and abstract features between the factors and target, deep learning has recently emerged as one of the useful tools with potential to predict the MSW amount. Therefore, this study aimed to design an MSW amount predicted system in Shanghai, consisting of Attention (A), one-dimensional convolutional neural network (C), and long short-term memory (L), to investigate the relationship between exogenous series (24 socioeconomics factors and past MSW amount) and target (MSW amount). The role of Attention, 1D-CNN, LSTM played on the MSW predicted amount also have investigated. The results show that attention is crucial for decoding the encoding information, which would improve performance between predicted and known MSW amount (R 2 in A-L-C, L-A-C, L-C-A was 89.45%, 90.77%, and 95.31%, respectively.). CNN modules appear to be positioned similarly across the MSW predicted system. Finally, R 2 in L-A-C, A-L-C, and A-C-L was 85.44%, 91.61%, and 89.45%, which suggested that LSTM as an intermediary between CNN and Attention modules seems a wise measure to predict the MSW amount based on the correlation efficiency. In addition, some socioeconomic factors including the average number of people in households and budget revenue may be chosen for the decision-making of MSW management in Shanghai city in the future, according to the weight of neurons in fully connected layers by the visual technology., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Carbonaceous fractions throughout the normal period and lockdown period (LP) before and during COVID-19 outbreak were analyzed in a polluted city, Zhengzhou, China. During LP, fine particulate matters, elemental carbon (EC), and secondary organic aerosol (SOC) concentrations fell significantly (29%, 32% and 21%), whereas organic carbon (OC) only decreased by 4%. Furthermore, the mean OC/EC ratio increased (from 3.8 to 5.4) and the EC fractions declined dramatically, indicating a reduction in vehicle emission contribution. The fact that OC1-3, EC, and EC1 had good correlations suggested that OC1-3 emanated from primary emissions. OC4 was partly from secondary generation, and increased correlations of OC4 with OC1-3 during LP indicated a decrease in the share of SOC. SOC was more impacted by NO 2 throughout the research phase, thereby the concentrations were lower during LP when NO 2 levels were lower. SOC and relative humidity (RH) were found to be positively associated only when RH was below 80% and 60% during the normal period (NP) and LP, respectively. SOC, Coal combustion, gasoline vehicles, biomass burning, diesel vehicles were identified as major sources by the Positive Matrix Factorization (PMF) model. Contribution of SOC apportioned by PMF was 3.4 and 3.0 μg/m 3 , comparable to the calculated findings (3.8 and 3.0 μg/m 3 ) during the two periods. During LP, contributions from gasoline vehicles dropped the most, from 47% to 37% and from 7.1 to 4.3 μg/m 3 , contribution of biomass burning and diesel vehicles fell by 3% (0.6 μg/m 3 ) and 1% (0.4 μg/m 3 ), and coal combustion concentrations remained nearly constant. The findings of this study highlight the immense importance of anthropogenic source reduction in carbonaceous component variations and SOC generation, and provide significant insight into the temporal variations and sources of carbonaceous fractions in polluted cities., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

Ozone (O 3 ) has become the primary pollutant in many cities, and high concentrations of O 3 cause significant harm to the ecological environment and human health. This study investigated the spatiotemporal distribution of surface concentrations of ozone over entire China and analyzed the influencing factors based on the geographical detector technique. Moreover, the Pearson correlation analysis was used to analyze the influence of various meteorological factors on ozone concentrations. The results showed that, on the national scale, the daily average O 3 concentration in the cities of China in 2019 was 92.441 μg/m 3 and the nonattainment rate of daily average ozone was 7.98%. However, the ozone nonattainment rate was 33.33% in heavily polluted regions. The highest O 3 concentration was observed in summer, and the lowest was observed in spring. The O 3 concentrations in cities across the country showed significant spatial distribution characteristics. Among the five pollutants, the highest correlation was observed between O 3 and PM 2.5 and the lowest was observed between O 3 and SO 2 . Among the metrological factors, wind speed and solar radiation are the most influencing factors, and showed positive correlation. Moreover, the annual precipitation is negatively correlated with O 3 -8h concentrations. The methods and findings of this paper can be used as an aid for air pollution control programs in different regions for diminishing the risk of exposure to various air pollutants., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

This paper aimed to analyze the composition and pollution sources of particulate matter (PM) in the Beijing-Tianjin-Hebei region and its surrounding areas (henceforth the BTH region) during the heating season to support the mitigation and control of regional air pollution. Manual monitoring data from the China National Environmental Monitoring Network for Atmospheric PM in the BTH region were collected and analyzed during the 2016 and 2018 heating seasons. The positive definite matrix factor analysis (PMF) model was used to analyze the PM sources in BTH cities during the heating season. The main PM components were organic matter (OM), nitrate (NO 3 - ), sulfate (SO 4 2- ) and ammonium salt (NH 4 + ). Direct emission sources have decreased since 2016, indicating the effectiveness of governmental controls on these sources; however, secondary pollution showed an increasing trend, suggesting control measures should be strengthened. Daily regional average concentrations of OM, SO 4 2- , NH 4 and EC concentrations were generally highest in the central and southern regions. The concentration of crustal substances was highest in some cities in the north and central parts of the BTH region. In the 2018 heating season, the pollution level of five transmission channels showed an increasing trend in the Northwest, Southeast, Yanshan, South and Taihang Mountain channels. These findings provide a scientific basis for the continued management of atmospheric PM pollution.+ , elemental carbon (EC), chloride (Cl - ) and trace elements all showed similar trends. When air quality worsened, the concentrations of the main PM components increased, but trends of change varied among components. In 2018, concentrations of OM and chloride were highest in the Taihang Mountains, and NO 3 concentrations were highest in Anyang, Hebi, Jiaozuo and Xinxiang. The SO 4 2- concentration was highest in the southern section of the Taihang Mountains. The NH 4 + and EC concentrations were generally highest in the central and southern regions. The concentration of crustal substances was highest in some cities in the north and central parts of the BTH region. In the 2018 heating season, the pollution level of five transmission channels showed an increasing trend in the Northwest, Southeast, Yanshan, South and Taihang Mountain channels. These findings provide a scientific basis for the continued management of atmospheric PM pollution., (Copyright © 2021 Elsevier Ltd. All rights reserved.)

Solid waste generation has rapidly increased due to the worldwide population, urbanization, and industrialization. Solid waste management (SWM) is a significant challenge for a society that arises local issues with global consequences. Thus, solid waste management strategies to recycle waste products are promising practices that positively impact sustainable goals. Several developed countries possess excellent solid waste management strategies to recycle waste products. Developing countries face many challenges, such as municipal solid waste (MSW) sorting and handling due to high population density and economic instability. This mismanagement could further expedite harmful environmental and socioeconomic concerns. This review discusses the current solid waste management and energy recovery production in developing countries; with statistics, this review provides a comprehensive revision on energy recovery technologies such as the thermochemical and biochemical conversion of waste with economic considerations. Furthermore, the paper discusses the challenges of SWM in developing countries, including several immediate actions and future policy recommendations for improving the current status of SWM via harnessing technology. This review has the potential of helping municipalities, government authorities, researchers, and stakeholders working on MSW management to make effective decisions for improved SWM for achieving sustainable development., (Copyright © 2021 Elsevier Ltd. All rights reserved.)