Machine learning insights into PM2.5 changes during COVID-19 lockdown: LSTM and RF analysis in Mashhad.

This study seeks to investigate the impact of COVID-19 lockdown measures on air quality in the city of Mashhad employing two strategies. We initiated our research using basic statistical methods such as paired sample t-tests to compare hourly PM2.5 data in two scenarios: before and during quarantine, and pre- and post-lockdown. This initial analysis provided a broad understanding of potential changes in air quality. Notably, a low reduction of 2.40% in PM2.5 was recorded when compared to air quality prior to the lockdown period. This finding highlights the wide range of factors that impact the levels of particulate matter in urban settings, with the transportation sector often being widely recognized as one of the principal causes of this issue. Nevertheless, throughout the period after the quarantine, a remarkable decrease in air quality was observed characterized by distinct seasonal patterns, in contrast to previous years. This finding demonstrates a significant correlation between changes in human mobility patterns and their influence on the air quality of urban areas. It also emphasizes the need to use air pollution modeling as a fundamental tool to evaluate and understand these linkages to support long-term plans for reducing air pollution. To obtain a more quantitative understanding, we then employed cutting-edge machine learning methods, such as random forest and long short-term memory algorithms, to accurately determine the effect of the lockdown on PM2.5 levels. Our models' results demonstrated remarkable efficacy in assessing the pollutant concentration in Mashhad during lockdown measures. The test set yielded an R-squared value of 0.82 for the long short-term memory network model, whereas the random forest model showed a calculated cross-validation R-squared of 0.78. The required computational cost for training the LSTM and the RF models across all data was 25 min and 3 s, respectively. In summary, through the integration of statistical methods and machine learning, this research attempts to provide a comprehensive understanding of the impact of human interventions on air quality dynamics.

Comprehensive characterization of unscientifically disposed municipal solid waste (MSW) in Kashmir Region, India.

Unscientific dumping of municipal solid waste (MSW) is a common practice in Kashmir. To have an environmentally friendly and sustainable waste management system, MSW was collected from nine study locations of this region. They were air-dried, then oven-dried at 105 °C for 24 h, segregated, and characterized for various components. The overall average organic waste was > 55%, plastic waste about 17%, inert material about 10%, paper 9%, and cloth waste 7%. The calorific value of paper and plastic wastes exhibited was 4910 kcal/kg, while organic waste had a calorific value of 1980 kcal/kg. The proximate analysis showed that the moisture content ranged from 16 to 29%, volatile matter ranged from 49 to 72%, ash content ranged from 0.03 to 5%, and fixed carbon ranged from 5 to 20%. In S7, the volatile matter content recorded the lowest value at 49.15%, while in S5, the volatile matter content was notably higher at 71.84%, indicating easier ignition. Further, elemental analysis revealed that the major elements in MSW were carbon and oxygen, 53% and 37%, respectively, with small traces of heavy metals with an average of 0.02% cadmium (Cd) and 0.006% lead (Pb). Moreover, field emission scanning electron microscopy (FESEM) micrographs provided confirmation that the majority of components in the MSW exhibited either partial or complete degradation, resulting in a rough surface texture. In addition, the presence of silica and other silicate groups was also detected. Fourier transform infrared spectroscopy (FT-IR) analysis revealed that the main functional groups were alcohol. In the X-ray diffraction (XRD) analysis, all the major mineral phases were detected between 20 and 30° 2θ, except for the peaks at 50-60° 2θ in S3 and S9 where catalysts such as zeolite Y and zeolite X were detected. Overall, the MSW had low moisture content but higher calorific value, making it a viable feedstock.

[Level and health evaluation of rare earth elements in human blood and hair in the mining areas in Northwest Hubei].

OBJECTIVE: To investigate the content of rare earth elements(REs)in blood and hair of residents in a RE mining area in Northwest Hubei, and evaluate the impact of REs on the health status of local residents. METHODS: A total of 191 residents from the core area of RE mining areas and 186 residents from non RE mining areas, aged 20-69, were selected. The content of REs in the blood and hair of the survey subjects was measured using inductively coupled plasma mass spectrometry, and compared with existing literature values. At the same time, blood tests and questionnaire surveys will be conducted on the health status of residents to examine whether human RE enrichment can lead to endemic diseases. RESULTS: The average total content of REs in the blood of residents in the mining area was 60.22 ng/mL, which was 3.35 times that of the control area; The average total content of REs in hair was 1197.91 ng/g, which was 6.32 times higher than the control area. As age increasing, the abundance of REs in the blood and hair of both men and women in mining areas increased. The proportion of Yttrium and Scandium in the blood and hair were much higher than that in the soil. Compared to hair, Yttrium and Scandium were more easily enriched in the blood. There was no significant difference in the probability of fatty liver, hepatitis B, hypoglycemia, hypotension, hypertension and heart disease and the average life span between residents in RE mining areas and those in the control area. CONCLUSION: The high daily average dietary intake of REs in residents leads to a relatively large accumulation of REs in human blood and hair, but no significant and substantial human health damage has been found at present.

Evaluation of the DGT passive samplers for integrating fluctuating concentrations of pharmaceuticals in surface water.

Diffusive gradients in thin films (DGTs) have been well-documented for the measurement of a broad range of organic pollutants in surface water. However, the performance has been challenged by the inherent periodic concentration fluctuations for most organic pollutants. Therefore, there is an urgent need to assess the true time-weighted average (TWA) concentration based on fluctuating concentration profiles. The study aimed to evaluate the responsiveness of DGT and accuracy of TWA concentrations, considering various concentration fluctuating scenarios of 20 pharmaceuticals in surface water. The reliability and accuracy of the TWA concentrations measured by the DGT were assessed by comparison with the sum of cumulative mass of DGT exposed at different stages over the deployment period. The results showed that peak concentration duration (1-5 days), peak concentration fluctuation intensity (6-20 times), and occurrence time of peak concentration fluctuation (early, middle, and late stages) have minimal effect on DGT's response to most target pharmaceutical concentration fluctuations (0.8 < CDGT/CTWA < 1.2). While the downward-bent accumulations of a few pharmaceuticals on DGT occur as the sampling time increases, which could be accounted for by capacity effects during a long-time sampling period. Additionally, the DGT device had good sampling performance in recording short fluctuating concentrations from a pulse event returning to background concentrations with variable intensity and duration. This study revealed a satisfactory capacity for the evaluation of the TWA concentration of pharmaceuticals integrated over the period of different pulse deployment for DGT, suggesting that this passive sampler is ideally suited as a monitoring tool for field application. This study represents the first trial for evaluating DGT sampling performance for pharmaceuticals with multiple concentration fluctuating scenarios over time, which would be valuable for assessing the pollution status in future monitoring campaign.

PFAS Contamination in Europe: Generating Knowledge and Mapping Known and Likely Contamination with "Expert-Reviewed" Journalism.

While the extent of environmental contamination by per- and polyfluoroalkyl substances (PFAS) has mobilized considerable efforts around the globe in recent years, publicly available data on PFAS in Europe were very limited. In an unprecedented experiment of "expert-reviewed journalism" involving 29 journalists and seven scientific advisers, a cross-border collaborative project, the "Forever Pollution Project" (FPP), drew on both scientific methods and investigative journalism techniques such as open-source intelligence (OSINT) and freedom of information (FOI) requests to map contamination across Europe, making public data that previously had existed as "unseen science". The FPP identified 22,934 known contamination sites, including 20 PFAS manufacturing facilities, and 21,426 "presumptive contamination sites", including 13,745 sites presumably contaminated with fluorinated aqueous film-forming foam (AFFF) discharge, 2911 industrial facilities, and 4752 sites related to PFAS-containing waste. Additionally, the FPP identified 231 "known PFAS users", a new category for sites with an intermediate level of evidence of PFAS use and considered likely to be contamination sources. However, the true extent of contamination in Europe remains significantly underestimated due to a lack of comprehensive geolocation, sampling, and publicly available data. This model of knowledge production and dissemination offers lessons for researchers, policymakers, and journalists about cross-field collaborations and data transparency.

Microalgal biochar assisted simultaneous removal of particulate matter, formaldehyde, and total volatile organic compounds (TVOC's) from indoor air.

Biochar-based materials for air treatment have gained significant attention for removing health-detrimental volatile organic compounds (VOCs) and particulate matter (PM) in indoor air settings. However, high turnaround time, multiple pretreatment processes involved, and high pore size and low surface area (>10 µm, <100 m2 g-1) of lignocellulosic feedstocks demand alternative biochar feedstock material. Considering this, we designed a simple first-of-its-kind indoor air scrubbing material using diatoms-enriched microalgae biochar. In the present study, the microalgae were cultivated on waste anaerobic digestate (biogas slurry) and were pyrolyzed at three different temperatures: 300 °C (BC300), 500 °C (BC500), and 700 °C (BC700). The BC500 and BC700 showed the highest removal efficiencies (99 %) for total volatile organic carbons (TVOCs) and formaldehyde (HCHO) at concentrations of 1.22 mg m-3 HCHO and 8.57 mg m-3 TVOC compared to 50% efficiency obtained with commercially available surgical, cloth, and N95 masks. The biochar obtained showed a high Brunauer-Emmett-Teller (BET) surface area of 238 m2 g-1 (BC500) and 480 m2 g-1 (BC700) and an average pore size of 9-11 nm due to the mesoporous characteristic of diatom frustules. The comparatively poor performance of BC300 was due to lower surface area (150 m2 g-1) arising from incomplete organic removal, as evidenced by FESEM-EDX and FTIR. The high removal efficiencies in BC500 and BC700 were also attributed to the presence of reactive functional groups such as -OH and R-NH2. Concurrently, the average particulate matter (PM10, PM2.5, and PM1) removal efficiency for BC500 and BC 700 ranged between 66 and 82.69 %. The PM removal performance of BC500 and BC700 was lower (15-20%) than commercially available masks. Overall, the present study highlights the importance of diatoms (reactive Si) present inside the pores of microalgal biochar for enhanced removal of PM, TVOCs, and HCHO at temperatures above 500 °C. This complete approach signifies a step towards establishing a self-sustainable and circular process characterized by minimal waste generation for indoor air treatment.

Effects of COVID-19 on coastal and marine environments: Aggravated microplastic pollution, improved air quality, and future perspective.

The COVID-19 pandemic during 2020-2023 has wrought adverse impacts on coastal and marine environments. This study conducts a comprehensive review of the collateral effects of COVID-19 on these ecosystems through literature review and bibliometric analysis. According to the output and citation analysis of these publications, researchers from the coastal countries in Asia, Europe, and America payed more attentions to this environmental issue than other continents. Specifically, India, China, and USA were the top three countries in the publications, with the proportion of 19.55%, 18.99%, and 12.01%, respectively. The COVID-19 pandemic significantly aggravated the plastic and microplastic pollution in coastal and marine environments by explosive production and unproper management of personal protective equipment (PPE). During the pandemic, the estimated mismanaged PPE waste ranged from 16.50 t/yr in Sweden to 250,371.39 t/yr in Indonesia. In addition, the PPE density ranged from 1.13 × 10-5 item/m2 to 2.79 item/m2 in the coastal regions worldwide, showing significant geographical variations. Besides, the emerging contaminants released from PPE into the coastal and marine environments cannot be neglected. The positive influence was that the COVID-19 lockdown worldwide reduced the release of air pollutants (e.g., fine particulate matter, NO2, CO, and SO2) and improved the air quality. The study also analyzed the relationships between sustainable development goals (SDGs) and the publications and revealed the dynamic changes of SDGs in different periods the COVID-19 pandemic. In conclusion, the air was cleaner due to the lockdown, but the coastal and marine contamination of plastic, microplastic, and emerging contaminants got worse during the COVID-19 pandemic. Last but not least, the study proposed four strategies to deal with the coastal and marine pollution caused by COVID-19, which were regular marine monitoring, performance of risk assessment, effective regulation of plastic wastes, and close international cooperation.

Integrated management of groundwater quantity, physicochemical properties, and microbial quality in West Nile delta using a new MATLAB code and geographic information system mapping.

Groundwater is an excellent alternative to freshwater for drinking, irrigation, and developing arid regions. Agricultural, commercial, industrial, residential, and municipal activities may affect groundwater quantity and quality. Therefore, we aimed to use advanced methods/techniques to monitor the piezometric levels and collect groundwater samples to test their physicochemical and biological characteristics. Our results using software programs showed two main types of groundwater: the most prevalent was the Na-Cl type, which accounts for 94% of the groundwater samples, whereas the Mg-Cl type was found in 6% of samples only. In general, the hydraulic gradient values, ranging from medium to low, could be attributed to the slow movement of groundwater. Salinity distribution in groundwater maps varied between 238 and 1350 mg L-1. Although lower salinity values were observed in northwestern wells, higher values were recorded in southern ones. The collected seventeen water samples exhibited brackish characteristics and were subjected to microbial growth monitoring. Sample WD12 had the lowest total bacterial count (TBC) of 4.8 ± 0.9 colony forming unit (CFU mg L-1), while WD14 had the highest TBC (7.5 ± 0.5 CFU mg L-1). None of the tested water samples, however, contained pathogenic microorganisms. In conclusion, the current simulation models for groundwater drawdown of the Quaternary aquifer system predict a considerable drawdown of water levels over the next 10, 20, and 30 years with the continuous development of the region.

Spatiotemporal variations, health risk assessment, and sources of potentially toxic elements in potamic water of the Anday Stream Basin (Türkiye), Black Sea Region.

Monitoring and protecting freshwater habitats are paramount for a sustainable water management perspective. This study investigated potentially toxic elements (PTEs) in the potamic water of the Anday Stream Basin (Türkiye), Black Sea Region, for a hydrological year (from May 2020 to April 2021). Among PTEs, the highest average values were recorded for sodium (Na) at 41.3 mg/L and the lowest for mercury (Hg) at 0.009 µg/L and noted under quality guidelines. The stream was found to be at the level of "Low Heavy Metal Pollution" and "Low Contamination" based on the ecotoxicological risk indices. The highest calculated hazard quotient (HQ) value of 1.21E-02 for Cd was noted in the children via the dermal pathway and the lowest of 6.91E-06 for Fe in adults via the ingestion pathway. Results revealed a higher hazard index (HI) value of 1.50E-02 for Cd to children and the lowest of 1.98E-05 for Fe to adults. As a result of applying agricultural risk indices, the stream showed sodium adsorption ratio values less than 6 and was found to be "Excellent" for agriculture. However, the sodium percentage values were less than 20 and found "Permissible" and the magnesium hazard > 50 and noted as "Unsuitable" for agriculture. Statistical analysis revealed that natural factors mainly attributed to PTE contamination of the Anday Stream Basin.

Bark thickness and related parameters of tree species along an elevation transect leading to treeline in Central Himalaya.

Since treelines are generally fire-free, the trees growing there are expected to have thin bark, unless adaptation to other factors than fire results in the selection of a thick bark. Related to this is also higher proportional investment in inner bark in such an environment of infrequent fire. This study has considered stem bark thickness both in absolute and relative terms and also in the frame of the composition of outer and inner bark components of 20 tree species along an elevation transect (2100-3300 m) in high ranges of the Central Himalaya leading to treelines. The study species varied from 2.1 to 16.2 mm for total bark thickness and from 1.2 to 18.85% for relative bark thickness. The average absolute total bark thickness across the tree species decreased with elevation from forest to treeline, both when trees of all diameters (10.2 ± 0.84 mm for forest and 6.9 ± 1.79 mm for treeline) and those of the same stem diameter range (18-20 m) were compared (9.10 ± 1.30 mm for forest species and 6.38 ± 1.31 mm for treeline species). Nevertheless, the treeline bark thickness was similar to those of several forest communities considered to have comparatively thick bark. Like many other biological structures, bark carries out multiple functions; therefore, its thickness could be affected by more than one environmental factor. We suggest that the requirement of mechanical resistance to the snowfall, rainstorms, wind and adaptation to a high sunlight and UV radiations or storage of water, and non-structural carbohydrates could affect total, outer and inner bark thickness. Studies on these aspects in similar ecosystems may help understand the multi-functional attributes of the bark. For trees of comparable sizes (trees with 18-20 cm diameter at breast height) treeline species also had lower relative bark thickness (< 6%) than trees of forest below it (> 7%). The median proportion of inner bark of the total bark (70.5%) for our 20 species was more than that for savannas (~ 50%), exposed to frequent fire regime and similar to those of in cool sclerophyllous forests and temperate rain forests where fire return time is > 100 years. However, it was lower than the inner bark proportion reported for tropical rain forests. To conclude, in spite of a fire-free environment, the Himalayan treeline and adjoining forest species show mixed bark characters.

Performance evaluation of deep learning based stream nitrate concentration prediction model to fill stream nitrate data gaps at low-frequency nitrate monitoring basins.

Accurate and frequent nitrate estimates can provide valuable information on the nitrate transport dynamics. The study aimed to develop a data-driven modeling framework to estimate daily nitrate concentrations at low-frequency nitrate monitoring sites using the daily nitrate concentration and stream discharge information of a neighboring high-frequency nitrate monitoring site. A Long Short-Term Memory (LSTM) based deep learning (DL) modeling framework was developed to predict daily nitrate concentrations. The DL modeling framework performance was compared with two well-established statistical models, including LOADEST and WRTDS-Kalman, in three selected basins in Iowa, USA: Des Moines, Iowa, and Cedar River. The developed DL model performed well with NSE >0.70 and KGE >0.70 for 67% and 79% nitrate monitoring sites, respectively. DL and WRTDS-Kalman models performed better than the LOADEST in nitrate concentration and load estimation for all low-frequency sites. The average NSE performance of the DL model in daily nitrate estimation is 20% higher than that of the WRTDS-Kalman model at 18 out of 24 sites (75%). The WRTDS-Kalman model showed unrealistic fluctuations in the estimated daily nitrate time series when the model received limited observed nitrate data (less than 50) for simulation. The DL model indicated superior performance in winter months' nitrate prediction (60% of cases) compared to WRTDS-Kalman models (33% of cases). The DL model also better represented the exceedance days from the USEPA maximum contamination level (MCL). Both the DL and WRTDS-Kalman models demonstrated similar performance in annual stream nitrate load estimation, and estimated values are close to actual nitrate loads.

Impact of regenerative farming practices on soil quality and yield of cotton-sorghum system in semi arid Indian conditions.

Regenerative agricultural practices, i.e. organic and natural farming, are rooted in India since ancient times. However, the high cost of production, lack of organic pest control measures and premium price of organic produces in chemical agriculture encourage natural farming. In the present study, the quality improvement of calcareous soils under organic (OGF) and natural (NTF) management was compared with integrated conventional (ICF) and non-invasive (NIF) farming practices with cotton-sorghum crops over three consecutive years. A total of 23 soil attributes were analyzed at the end of the third cropping cycle and subjected to principal component analysis (PCA) to select a minimum data set (MDS) and obtain a soil quality index (SQI). The attributes soil organic carbon (SOC), available Fe, pH, bulk density (BD) and alkaline phosphatase (APA) were selected as indicators based on correlations and expert opinions on the lime content of the experimental soil. The SQI was improved in the order of OGF (0.89) > NTF(0.69) > ICF(0.48) > NIF(0.05). The contribution of the indicators to SQI was in the order of available Fe (17-44%) > SOC (21-28%), APA (11-36%) > pH (0-22%), and BD (0-20%) regardless of the farming practices. These indicators contribute equally to soil quality under natural (17-22%) and organic (18-22%) farming. The benefit:cost ratio was calculated to show the advantage of natural farming and was in the order of NTF(1.95-2.29), ICF (1.34-1.47), OGF (1.13-1.20) and NIF (0.84-1.47). In overall, the natural farming significantly sustained the soil quality and cost benefit compared to integrated conventional farming practices.

A promising approach for the removal of hexavalent and trivalent chromium from aqueous solution using low-cost biomaterial.

Heavy metal pollution is an enduring environmental challenge that calls for sustainable and eco-friendly solutions. One promising approach is to harness discarded plant biomass as a highly efficient environmental friendly adsorbents. In this context, a noteworthy study has spotlighted the employment of Euryale ferox Salisbury seed coat (E.feroxSC) for the exclusion of trivalent and hexavalent chromium ions. This study aims to transform discarded plant residue into a novel, environmentally friendly, and cost-effective alternative adsorbent, offering a compelling alternative to more expensive adsorption methods. By repurposing natural materials, we can contribute to mitigating heavy-metal pollution while promoting sustainable and economically viable solutions in environmental remediation. The effect of different parameters, i.e., chromium ions' initial concentration (5-25 mg L-1), solution pH (2-7), adsorbent dosage (0.2-2.4 g L-1), contact time (20-240 min), and temperature (298-313 K), were investigated. E.feroxSC proved highly effective, achieving 96.5% removal of Cr(III) ions at pH 6 and 97.7% removal of Cr(VI) ions at pH 2, with a maximum biosorption capacity of 18.33 mg/g for Cr(III) and 13.64 mg/g for Cr(VI), making it a promising, eco-friendly adsorbent for tackling heavy-metal pollution. The adsorption process followed the pseudo-second-order kinetic model, aligning well with the Langmuir isotherm, exhibited favorable thermodynamics, and was characterized as feasible, spontaneous, and endothermic with physisorption mechanisms. The investigation revealed that E.feroxSC effectively adsorbed Cr(VI) which could be rejuvenated in a basic solution with minimal depletion in its adsorption capacity. Conversely, E.feroxSC's adsorption of Cr(III) demanded rejuvenation in an acidic milieu, exhibiting comparatively less efficient restoration.

Indoor sulfur dioxide prediction through air quality modeling and assessment of sulfur dioxide and nitrogen dioxide levels in industrial and non-industrial areas.

In this study, the levels of sulfur dioxide (SO2) and nitrogen dioxide (NO2) were measured indoors and outdoors using passive samplers in Tymar village (20 homes), an industrial area, and Haji Wsu (15 homes), a non-industrial region, in the summer and the winter seasons. In comparison to Haji Wsu village, the results showed that Tymar village had higher and more significant mean SO2 and NO2 concentrations indoors and outdoors throughout both the summer and winter seasons. The mean outdoor concentration of SO2 was the highest in summer, while the mean indoor NO2 concentration was the highest in winter in both areas. The ratio of NO2 indoors to outdoors was larger than one throughout the winter at both sites. Additionally, the performance of machine learning (ML) approaches: multiple linear regression (MLR), artificial neural network (ANN), and random forest (RF) were compared in predicting indoor SO2 concentrations in both the industrial and non-industrial areas. Factor analysis (FA) was conducted on different indoor and outdoor meteorological and air quality parameters, and the resulting factors were employed as inputs to train the models. Cross-validation was applied to ensure reliable and robust model evaluation. RF showed the best predictive ability in the prediction of indoor SO2 for the training set (RMSE = 2.108, MAE = 1.780, and R2 = 0.956) and for the unseen test set (RMSE = 4.469, MAE = 3.728, and R2 = 0.779) values compared to other studied models. As a result, it was observed that the RF model could successfully approach the nonlinear relationship between indoor SO2 and input parameters and provide valuable insights to reduce exposure to this harmful pollutant.

Physicochemical properties and heavy metal concentration of soils along Enyigba mining site in Ikwo, Ebonyi State Nigeria.

This study assessed the physicochemical properties of soils and their levels of heavy metal contents in soils along the Enyigba mining site in Ikwo, Ebonyi State, Nigeria. A total of 96 samples of soil were taken at depths of 0 to 20 cm using a soil auger and core sampler at a horizontal spacing of 100 m between each location and examined using standard laboratory techniques. The control soil samples were taken from the Alex Ekwueme Federal University Experimental and Research Farm in Ebonyi State, at a distance of 50 m from each spot at a depth of 0 to 20 cm. The results obtained from this study showed significant variations in the physicochemical properties and heavy metal levels of the soil from the Enyigba mining site, indicating that the mining activities have contaminated the soil. The result also indicated that mining operations may be responsible for the increase in sand and the decrease in silt and clay particles. The mining site's pH was typically low, indicating that the soil is naturally acidic. The contamination indices showed that lead recorded very high contamination factor of 27.068, while iron, nickel and zinc were low. The observed high concentration factor of lead had an impact on the soil's bulk density, saturated hydraulic conductivity, total porosity, calcium, potassium ion, magnesium ion, total nitrogen, organic carbon, cation exchange capacity, phosphorus and base saturation contents. It is recommended that the government's Ministry of Environment, at all levels, take a proactive stance in managing the excessive and subpar mining operations in the study area.

Adsorption behavior and the potential risk of As(V) in soils: exploring the effects of representative surfactants.

Arsenic contamination in soils poses a critical global challenge, yet the influence of surfactants on arsenic adsorption behavior is often underestimated. This study aims to investigate the effects of three representative surfactants, namely cetyltrimethylammonium bromide (CTAB), sodium dodecyl sulfate (SDS), and polyethylene glycol anhydrous sugar alcohol monooleate (Tween 80), on arsenic adsorption behavior in soils. The adsorption isotherm shifts from a single Temkin model without surfactants to both the Langmuir and Temkin models in the presence of surfactants, indicating the simultaneous occurrence of monolayer and multilayer adsorption for arsenic in soils. Moreover, the surfactants can inhibit the adsorption and hasten the attainment of adsorption equilibrium. SDS displayed the most inhibitory effect on arsenic adsorption, followed by Tween 80 and CTAB, due to the competitive adsorption, electrostatic interaction, and hydrophobic interaction. Variations in zeta potential with different surfactants further elucidate this inhibitory phenomenon. Through orthogonal experiment analyses, pH emerges as a primary factor influencing arsenic adsorption in soils, with surfactant concentration and type identified as secondary factors. Temperature notably affects CTAB, with the adsorption inhibition rate plummeting to a mere 0.88% at 50 °C. Sequential extraction analysis revealed that surfactants enhanced the bioavailability of arsenic. The FTIR, XRD, SEM, and CA analyses further support the mechanism underlying the effect of surfactants on arsenic adsorption in soil. These analyses indicate that surfactants modify the composition and abundance of functional groups, hinder the formation of arsenic-containing substances, and improve soil compactness, smoothness, and hydrophilicity. This study provides valuable insights into the effect of surfactants in arsenic-contaminated soils, which is often ignored in previous work.

High spatial-resolved source-specific exposure and risk in the city scale: Influence of spatial interrelationship between PM2.5 sources and population on exposure.

Research on High Spatial-Resolved Source-Specific Exposure and Risk (HSRSSER) was conducted based on multiple-year, multiple-site synchronous measurement of PM2.5-bound (particulate matter with aerodynamic diameter<2.5 µm) toxic components in a Chinese megacity. The developed HSRSSER model combined the Positive Matrix Factorization (PMF) and Land Use Regression (LUR) to predict high spatial-resolved source contributions, and estimated the source-specific exposure and risk by personal activity time- and population-weighting. A total of 287 PM2.5 samples were collected at ten sites in 2018-2020, and toxic species including heavy metals (HMs), polycyclic aromatic hydrocarbons (PAHs) and organophosphate esters (OPEs) were analyzed. The percentage non-cancer risk were in the order of traffic emission (48 %) > industrial emission (22 %) > coal combustion (12 %) > waste incineration (11 %) > resuspend dust (7 %) > OPE-related products (0 %) ≈ secondary particles (0 %). Similar orders were observed in cancer risk. For traffic emission, due to its higher source contributions and large population in central area, non-cancer and cancer risk fraction increased from 23 % to 48 % and 20 % to 46 % after exposure estimation; while for industrial emission, higher source contributions but small population in suburb area decreased the percentage non-cancer and cancer risk from 38 % to 22 % and 39 % to 24 %, respectively.

Organohalogen compounds in a hotspot for chemical pollution: Assessment in free-ranging Atlantic spotted dolphins (Stenella frontalis).

The assessment of chemical pollution in free-ranging living mammals is viable using remote biopsies and portrays a comprehensive scenario of environmental health. The Southwestern Atlantic Ocean holds incredible biodiversity, but it is under the constant and invisible threat of persistent organic pollutants (POPs) of anthropogenic origin, such as pesticides, brominated flame retardants, and industrial-use compounds (e.g., PCBs). Thus, this study aimed to assess the bioaccumulation of POPs (PCBs, DDTs, HCB, mirex and PBDEs) and natural organobromine compounds (MeO-BDEs) using gas-chromatography coupled to mass spectrometry in biopsy samples of Atlantic spotted dolphins (Stenella frontalis, n = 20) that inhabit and forage both inside and in adjacent areas to degraded (Guanabara Bay) and conserved (Ilha Grande Bay) coastal bays in the Southeastern Brazil. Among the studied compounds, PCBs were predominant in the contamination profile with median concentration of 97.0 µg.g-1 lipid weight (lw), followed by the sum of the p,p' isomers of DDT, DDD, and DDE of 11.0 µg.g-1 lw, the brominated flame retardants PBDEs of 1.6 µg.g-1 lw, and the other organochlorine pesticides mirex of 0.78 µg.g-1 lw, and HCB of 0.049 µg.g-1 lw. The MeO-BDEs were detected with a median concentration of 22.8 µg.g-1 lw. 85 % of the Atlantic spotted dolphins analyzed in this study presented PCB concentration that exceeded even the less conservative threshold limits for adverse health effects (41 µg.g-1 lw). This study shows that despite the conservation status of preserved bays, cetacean species foraging in these locations are still under increased threat. Hence chemical pollution demands local and global efforts to be mitigated.

Differences in aerosol chemistry at a regional background site in Hong Kong before and during the COVID-19 pandemic.

Restrictions on human-related activities implemented in Hong Kong to curb the spread of the coronavirus disease 2019 (COVID-19) pandemic provided an opportunity to investigate the anthropogenic impact on organic aerosols (OA) composition. In this study, we conducted a comparative analysis of online measurements of non-refractory submicron particulate matters (NR-PM1) at a regional background site in Hong Kong, covering the periods before the COVID-19 control (November 2018) and during the COVID-19 control (October to November 2020), to investigate changes in OA sources and formation mechanisms. Among the measured NR-PM1 components, organics were the most dominant species with an average percentage of 51.0 ± 0.5 %, exceeding pre-control levels of 44.0 ± 0.7 %. Moreover, 88 % of the organics were attributed to oxygenated OA (OOA). Diurnal variations of all bulk components in NR-PM1 consistently showed afternoon peaks, indicating photochemical processes during COVID-19 control. Similar to the pre-restriction period, the positive matrix factorization (PMF) model showed that OOA was composed of three factors, including two less-oxidized oxygenated factors (LO-OOA1 and LO-OOA2) and one more-oxidized oxygenated factor (MO-OOA). The contribution of the LO-OOA2 factor remained small and stable during both sampling campaigns, which might imply background levels of OOA at this site. The formation of the two predominant components of organics (e.g., LO-OOA1 and MO-OOA) was further discussed. Compared with before control, observational evidence showed that the levels of MO-OOA exceeded LO-OOA1 during the control period, and the average concentration of odd oxygen (Ox = ozone + nitrogen dioxide) increased by 53 % during the COVID-19 control. Besides, the results showed that both LO-OOA1 and MO-OOA exhibited similar diurnal variations to Ox, and their concentrations generally enhanced with increasing Ox levels. This suggested that the formation of OOA was closely related to the photochemical oxidation processes when anthropogenic emissions were reduced. By correlating LO-OOA1 and MO-OOA with speciated OA markers, we found that the formation of LO-OOA1 remained associated with anthropogenic sources, while biogenic emissions contributed to the formation of MO-OOA during the COVID-19 control. Our findings highlight the interplay between emissions, atmospheric conditions, and aerosol composition, providing valuable insights to guide strategic decisions for future air quality improvement.

Modeling assessment of air pollution control measures and COVID-19 pandemic on air quality improvements over Greater Bay Area of China.

A remarkable progress has been made toward the air quality improvements over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) of China from 2017 to 2020. In this study, for the first time, the emission reductions of regional control measures together with the COVID-19 pandemic were considered simultaneously into the development of the GBA's emission inventories for the years of 2017 and 2020. Based on these collective emission inventories, the impacts of control measures, meteorological variations together with temporary COVID-19 lockdowns on the five major air quality index pollutants (SO2, NO2, PM2.5, PM10, and O3, excluding CO) were evaluated using the WRF-CMAQ and SMAT-CE model attainment assessment tool over the GBA region. Our results revealed that control measures in the Pearl River Delta (PRD) region affected significantly the GBA, resulting in pollutant reductions ranging from 48 % to 64 %. In contrast, control measures in Hong Kong and Macao contributed to pollutant reductions up to 10 %. In PRD emission sectors, stationary combustion, on-road, industrial processes and dust sectors stand out as the primary contributors to overall air quality improvements. Moreover, the COVID-19 pandemic during period I (Jan 23-Feb 23) led to a reduction of NO2 concentration by 7.4 %, resulting in a negative contribution (disbenefit) for O3 with an increase by 2.4 %. Our findings highlight the significance of PRD control measures for the air quality improvements over the GBA, emphasizing the necessity of implementing more refined and feasible manageable joint prevention and control policies.