Pollution Sources Research Articles

Particulate matter pollution in central Bangkok: assessing outdoor workers’ perceptions and exposure

ABSTRACT Air pollution is estimated to cause 32,300 deaths annually in Thailand, with PM2.5 exposure accounting for much of this. However, not everyone bears the burden of exposure to air pollution equally, and outdoor workers are particularly exposed. In this study, we sought to better understand how local residents and outdoor workers in central Bangkok experience air pollution in their day-to-day life, through a survey of their symptoms and mitigation measures. Respondents were aware of the risks of air pollution but felt there was little they could do about their exposure to it. We also asked a subset of respondents to carry a portable PM2.5 sensor during their working day, to assess which occupations and locations faced highest pollution exposure. We found that all workers, including those who didn’t always work outdoors, faced exposure that exceeded both the WHO and Thailand 24-hour PM2.5 standards, at 43.94 μg/m3 on average. These findings demonstrate that workers face harmful levels of daily exposure and should be provided with appropriate mitigating measures by their employers to limit their exposure, and targeted health screenings. At a city and national scale, concrete action is needed to reduce sources of pollution.

Water Quality Assessment of the Euphrates River from Haditha to Al-Nasiriyah, Iraq

A comprehensive assessment of four gauging stations along the Euphrates River reach (Haditha, Al-Hindiya, Samawa, and Al-Nasiriyah) was conducted to assess the water quality deterioration spatially and temporarily for the period 2005-2021. The spotlight is on discharge rates and climate conditions, which mainly controlling water quality and availability. The mean monthly discharge rate was taken 2005–2021, in addition to the water quality parameters (TDS, pH, EC, Mg+2, Ca+2, Na+, SO4-2, Cl-, and HCO3-, BOD5, COD, FCB, and E. coli). The discharge rates showed a visible decrease downstream, causing the river salinity and all the ion concentrations to rise. The statistical package of social sciences revealed two groups of water pollution sources: The F1 reflects natural conditions produced from the dissolution of the basin rocks, with a percentage of 28.0% from the total variance; the eigenvalue is 4.4. F2 reflects the anthropogenic sources, contributing 14.5% and having an eigenvalue of 2.1. The total ionic salinity plot indicated less than 2000 mg/l at the Haditha and Al-Hindiya sites and between 3000 and 4000 mg/l at downstream sites (Samawa and Al-Nasiriyah). The transportation of dissolved loads is decreased downstream following the discharge rate as indicated by the rating curves, as follows: 781000, 542628.4, 443861, and 432950.4 tons/month for Haditha, Al-Hindiya, Samawa, and Al-Nasiriyah, respectively. Water type shows a notable downstream deterioration, from MgSO4 at Haditha and Na2SO4 at Al-Hindiya to NaCl at Samawa and Al-Nasiriyah sites.

Utilization of cow dung for biogas to improve livestock breeders and MSME entrepreneurs' economy

Sriwulan Village, located in Limbangan District, Kendal Regency, is home to both cattle farmers and several Micro, Small, and Medium Enterprises (MSMEs). The village has a farmer group named Ngudi Kawruh, managing around 20 cows. However, cow dung in the area has become a source of air pollution, and the four MSMEs still rely on Liquefied Petroleum Gas (LPG) for production. One effective solution to this issue is converting cow dung into biogas, which offers both environmental and economic benefits. This community service program aims to introduce appropriate technology for processing cow dung into biogas using biodigesters. The biogas is stored in portable containers, such as truck inner tires, while the biodigester output, in the form of slurry, is utilized as solid organic fertilizer. The program provides education on these concepts through posters, videos, demonstrations, and YouTube links to ensure broad community understanding. By implementing this technology, the program not only addresses environmental concerns and reduces dependence on LPG but also significantly enhances the economic potential of both the villagers and MSME entrepreneurs, promoting sustainable development.

Disposal of used oils as a prospective method of production of biodiesel

The article is devoted to the improvement of the production technology of diesel biofuel from waste oils, which are a significant source of environmental pollution. Environmental pollution is one of the biggest environmental problems of our time. Used oils that are not properly disposed of cause serious environmental problems, including water and soil pollution. The use of waste oils for the production of biofuel is a promising technology that allows reducing the amount of pollution and reducing dependence on fossil fuels. Biodiesel from waste oils has significant environmental, economic and sustainable advantages. The main goal of the article is a detailed analysis of modern technologies for the production of diesel biofuel from waste oils and an assessment of their improvements and impact on ecology, economy and society. The article discusses innovative approaches to the processing of used oils, their purification and preparation for further use. An analysis of the main methods of transesterification, hydrogenation, esterification and enzyme catalysis, as well as the latest technologies, such as ultrasonic and microwave intensification of biofuel production, was carried out. New technological solutions for the preliminary preparation of used oils with a high content of free fatty acids using a combination of acid catalysts are proposed, and a technological scheme of the full production cycle is developed. The rational parameters of the equipment for the preliminary preparation of used oils with a high content of free fatty acids have been determined. Recommended conditions include hydrogenation temperature not higher than 80°С, duration of the process not less than 40 minutes; separation of the water-protein part by centrifugation at a rotation frequency of the centrifuge rotor of 3000 rpm for 20 minutes; the esterification reaction temperature is no more than 60°C; molar ratio of alcohol to oil 9:1; acid catalyst concentration within 1-15%; the intensity of mixing in the reactor is 31.42 s-1; the duration of the process is not less than 120 minutes. It was established that it is advisable to use potassium hydroxide for the transesterification reaction. The use of potassium hydroxide is beneficial because the potassium salts formed during the technological process of diesel biofuel production can be used as mineral fertilizers. According to the results of the research, the optimal parameters for the transesterification reaction were chosen: the amount of methanol - 20% by mass. from the weight of the oil, the KOH 1 catalyst is 1.5%, the temperature of the process is 60°C and the duration is 60-70 minutes. Examples of successful implementation of these technologies in various countries of the world, in particular in Europe, the USA and Asia, are presented. Additionally, recommendations are provided for further research and technology development, including the need to improve waste oil purification methods, optimize transesterification processes, and integrate renewable energy sources. The prospects and challenges of the industry of biofuel production from waste oils are considered, in particular the issues of regulatory support, financial incentives and investment attraction.

Does exposure timing of macrolide antibiotics affect the development of river periphyton? Insights into the structure and function

Discharged sewage is the dominant source of urban river pollution. Macrolide antibiotics have emerged as prominent contaminants, which are frequently detected in sewage and rivers and pose a threat to aquatic microbial community. As a typical primary producer, periphyton is crucial for maintaining the biodiversity and functions of aquatic ecosystem. However, effects of antibiotic exposure time as well as the recovery process of periphyton remain undetermined. In the present study, five exposure scenarios of two typical macrolides, erythromycin (ERY) and roxithromycin (ROX) were investigated at 50 µg/L, dose to evaluate their potential detrimental effects on the structure and function of periphyton and the subsequent recovery process in 14 days. Results revealed that the composition of periphytic community returned to normal over the recovery period, except for a few sensitive species. The antibiotics-caused significant photodamage to photosystem II, leading to continuous inhibition of the photosynthetic capacity of periphyton. Furthermore, no significant difference in carbon metabolism capacity was observed after direct antibiotic exposure, while the amine carbon utilization capacity of periphyton remarkably increased during the recovery process. These results indicated that periphyton community was capable of coping with the periodic exposure of antibiotic pollutants and recovering on its own. However, the ecological functions of periphyton can be permanently disturbed due to macrolide exposure. Overall, this study sheds light on the influence of macrolide exposure on the development, structure and function of the periphytic microbial community in rivers.

Assessment of water quality and source apportionment of pollution in a tropical river in eastern India: A study utilizing multivariate statistical tools and the APCS-MLR receptor model.

The Mundeswari River, an ecologically distressed river in eastern India, has been subjected to water quality deterioration largely due to anthropogenic activities in its vicinity. This study aimed to comprehensively evaluate the current state of pollution in the river and assess the appropriateness of river water for irrigation, given its extensive use for agricultural purposes. Monthly water quality monitoring was undertaken at four distinct sampling sites (SP1-SP4) over a two-year period (2020-2022), considering seventeen water quality parameters. This research employed principal component analysis/factor analysis (PCA/FA) and absolute principal component score-multiple linear regression (APCS-MLR) receptor modelling. These methodologies were used to discern and quantify potential sources of pollution influencing the water quality of the Mundeswari River. The study revealed that the water quality of the Mundeswari River was most degraded during the pre-monsoon season. Among the four sampling sites, SP3 exhibited the highest level of pollution with mean biochemical oxygen demand (BOD) and chemical oxygen demand (COD) values of 5.36mg/L and 44.72mg/L, respectively. According to the one-way analysis of variance (ANOVA), there was considerable spatial and seasonal disparities (P < 0.05) in most water quality parameters. The PCA/FA extracted four latent pollution sources, accounting for 81.5% of the total variance. The primary factors influencing the quality of river water are natural weathering processes, discharge of domestic effluent and waste, and agricultural runoff. The APCS-MLR receptor model further revealed that agricultural drainage factors and the discharge of domestic effluent and waste had a greater impact on the Mundeswari River. The investigation concluded that the mean values of all indicators for irrigation suitability were below the defined threshold limits, indicating that the water of the studied river appears suitable for irrigation. The outcomes of this study may significantly contribute to the formulation of sustainable strategies for the ecological rejuvenation of the Mundeswari River.

The Impacts of COVID-19 Lockdowns on Road Transport Air Pollution in London: A State-Space Modelling Approach.

The emergence of the COVID-19 pandemic in 2020 led to the implementation of legal restrictions on individual activities, significantly impacting traffic and air pollution levels in urban areas. This study employs a state-space intervention method to investigate the effects of three major COVID-19 lockdowns in March 2020, November 2020, and January 2021 on London's air quality. Data were collected from 20 monitoring stations across London (central, ultra-low emission zone, and greater London), with daily measurements of NOx, PM10, and PM2.5 for four years (January 2019-December 2022). Furthermore, the developed model was adjusted for seasonal effects, ambient temperature, and relative humidity. This study found significant reductions in the NOx levels during the first lockdown: 49% in central London, 33% in the ultra-low emission zone (ULEZ), and 37% in greater London. Although reductions in NOx were also observed during the second and third lockdowns, they were less than the first lockdown. In contrast, PM10 and PM2.5 increased by 12% and 1%, respectively, during the first lockdown, possibly due to higher residential energy consumption. However, during the second lockdown, PM10 and PM2.5 levels decreased by 11% and 13%, respectively, and remained unchanged during the third lockdown. These findings highlight the complex dynamics of urban air quality and underscore the need for targeted interventions to address specific pollution sources, particularly those related to road transport. The study provides valuable insights into the effectiveness of lockdown measures and informs future air quality management strategies.

Quantifying seasonal variations in pollution sources with machine learning-enhanced positive matrix factorization

As the pace of industrialization and urbanization accelerates, water quality management faces increasing challenges, with traditional methods for pollutant source apportionment often proving inadequate in handling complex environmental data. This study enhances the precision and reliability of pollutant source identification by integrating Positive Matrix Factorization (PMF) models with diverse machine learning techniques. Utilizing data from 17 water quality monitoring stations along the Wuding River from 2017 to 2021, we employed Random Forest (RF), Support Vector Machine (SVM), Elastic Net (EN), and Extreme Gradient Boosting (XGBoost) models to predict the Water Quality Index (WQI) during dry and wet seasons. Results indicate that the RF model exhibited optimal performance in the dry season (R2 = 0.93), while the SVM was superior in the wet season (R2 = 0.94). SHAP (SHapley Additive exPlanations) value analysis identified CODMn and NH3-N as significant influencers on WQI in the dry season, whereas COD, BOD, and TP gained prominence during the wet season. SHAP values reveal the contribution of each feature to the model output, thereby enhancing the model’s transparency and interpretability. Additionally, feature importance identified by machine learning was utilized as weights to optimize the contribution rates predicted by the PMF model. The optimised model was able to identify the contribution of domestic and farm effluent discharges more accurately in the dry season, with a significant increase in the percentage of identification from 19.4 % to 45.4 %, and an increase in the percentage of contribution from agricultural non-point sources and domestic effluent in the rainy season. This research offers a novel perspective on the characteristics of river water pollution and holds significant implications for formulating data-driven environmental management strategies.

Source-oriented health risk assessment of groundwater based on hydrochemistry and two-dimensional Monte Carlo simulation

Accurately assessing the health risks posed by major contaminants is essential for protecting groundwater. However, the complexity of pollution sources and the uncertainty of parameters pose challenges for quantitative health risk assessment. In this study, a source-oriented groundwater risk evaluation process was improved by screening key pollutants, employing a combined hydrochemical and positive matrix factorization (PMF) approach for source apportionment, and incorporating two-dimensional Monte Carlo simulation for risk characterization. The application of this process to groundwater assessment in Central Jiangxi Province identified NO3-, F-, Se and Mn as the key pollutants. The pollution sources were anthropogenic activities, rock dissolution, regional geological processes, and ion exchange. Anthropogenic sources contributed 36.8 % and 28.8 % of the pollution during the wet season and dry season, respectively, and accounted for more than half of the health risks. NO3- from anthropogenic sources was the primary controlling pollutant. Additionally, the risk assessment indicated that children were at the highest health risk during the dry season, with ingestion rate suggested to be controlled below 1.062 L·day−1 to make the health risk within an acceptable range. The improved assessment methodology could provide more accurate results and recommended intakes.

Yearly variations of water-soluble ions over Xi'an, China: Insight into the importance contribution of nitrate to PM2.5

Water-soluble inorganic ions (WSIIs) in PM2.5 play an important role in the formation of air pollution, which in turn affects climate change and human health. The formation pathways and factors influencing WSIIs have received extensive attention. Here, we analyzed the contents of nine WSIIs in PM2.5, collected from 2015 to 2021 in Xi'an City, China, with the aim of investigating long-term atmospheric pollution changes. Sulfate (SO42−), nitrate (NO3−), and ammonium (NH4+) together contributed to 66.8%–88.1% and Ca2+ accounted for 5.1%–13.1% of total WSIIs. The relative content of SO42− exhibited a gradually decreasing trend (from 49.80% in 2015 to 29.98% in 2021), whereas NO3− was increased in the same time period (from 13.96% in 2015 to 29.92% in 2021). In addition, the nitrogen oxidation rate showed an annual increase in this period, whereas the sulfur oxidation rate decreased, and their fitted curves intersected in 2019. The key finding of this study is that the air pollution pattern in Xi'an has changed from sulfate-dominated to nitrate-dominated particles, as evidenced by the feature importance results of the random forest model. We propose that more attention should be paid to vehicle emissions and road dust as pollution sources. Overall, the findings of this study serve as a useful reference to aid relevant authorities in devising more effective policies for controlling PM2.5 pollution at its source.

Environmental injustice in the air quality for digital platform delivery workers in Bogotá, Colombia, 2021

Air quality is a matter of interest for public health due to its rapid deterioration in low- and middle-income countries and the effects of polluted air on the health of populations. To explore the air quality conditions in which digital platform delivery workers carry out their work, evaluating the localities of Kennedy and Usaquén in Bogotá, 2021. We developed a mixed parallel convergent study based on four sources of information: 1) Ethnographic observation in five commercial locations of the two localities; 2) Monitoring of PM10 and PM2.5 in 56 delivery routes using a low-cost sensor; 3) Daily logs of the routes to support the device data interpretation, and 4) A semi-structured interview applied to the drivers to explore their danger perception during the routes. We identified elements causing environmental injustice among digital platform delivery workers between the two study locations. The routes made by the delivery drivers in the locality of Kennedy registered higher concentrations of PM10 and PM2.5, compared to the values observed in Usaquén. The sources of air pollution identified by the delivery drivers through ethnographic observation and the router logbook showed the worst parameters in Kennedy. We evidenced that air quality, urban equipment, road infrastructure, mobile sources, and geospatial location are elements that mark the presence of environmental injustice for the digital platform delivery drivers in the studied localities. To reduce this inequity, it is necessary for digital delivery platforms and the district government to implement strategies that reduce the exposure and emission of air pollutants to protect the health of digital platform delivery workers.

Effect of multi-component gas on removal of trace hydrogen sulfide activity from blast furnace gas using activated carbon adsorbent

Abstract In the steel industry, blast furnace gas (BFG) is huge with complex components. The existence of hydrogen sulfide (H2S) in the BFG can produce sulfur dioxide (SO2) after combustion, which will increase the source of SO2 pollution and make the desulphurization more difficult, to be threat to people health. At present, the removal of H2S by dry adsorption with modified activated carbon adsorbent is a high-precision and low-cost desulphurization method. However, the effect of complex gas components in BFG on the adsorption of H2S by activated carbon adsorbent is not sufficient. Based on the fixed bed adsorbent evaluation system, a new type of highly efficient copper-cerium oxide (Cu–Ce–O) modified activated carbon H2S adsorbent was developed. And the effects of carbon monoxide (CO), carbon dioxide (CO2), hydrogen (H2), oxygen (O2), sulfur dioxide and hydrogen chloride (HCl) in BFG on the desulfurization activity of adsorbents were investigated. The results showed that the performance of H2S removal decreased in the presence of CO, CO2, HCl and SO2, and improved in the presence of H2 and O2. Other parameters were also studied which might influence the process. The application of modified activated carbon adsorbent in simulated BFG is basically stable. According to the fitting results of adsorption kinetics for the five adsorption models, as the atmosphere becomes BFG from N2, pore diffusion becomes the main adsorption form. However, the effects of internal diffusion, chemical adsorption and external mass transfer decreased. The Bangham model is the most suitable model to describe H2S adsorption process.

Water quality assessment and its pollution source analysis from spatial and temporal perspectives in small watershed of Sichuan Province, China.

Rapid socio-economic development has led to many water environmental issues in small watersheds such as non-compliance with water quality standards, complex pollution sources, and difficulties in water environment management. To achieve a quantitative evaluation of water quality, identify pollution sources, and implement refined management in small watersheds, this study collected monthly seven water quality indexes of four monitoring points from 2010 to 2023, and ten water quality indexes of 23 sampling points in the Shiting River and Mianyuan River which are tributaries of the Tuojiang River Basin. Then, water quality evaluation and pollution source analysis were conducted from both temporal and spatial perspectives using the Water Quality Index (WQI) method, the Absolute Principal Component Scores/Multiple Linear Regression (APCS-MLR) method, and the Positive Matrix Factorization (PMF) receptor modeling technique. The results indicated that except for total nitrogen (TN), the concentrations of other water quality indexes exhibited a decreasing trend, and all were divided into two obvious stages before and after 2016. Furthermore, the proportion of water quality grade of Good and above increased from 73.96 to 84.94% from 2010-2015 to 2016-2023, and the water quality grade of Good and above from upstream to downstream dropped from 100 to 23.33%. From the temporal scale, four and five pollution sources were identified in the first and second stages, respectively. The distinct TN pollutant is mainly affected by agricultural non-point sources (NPS), whose impact is enhanced from 17.76 to 78.31%. Total phosphorus (TP) was affected by the phosphorus chemical industry, whose contribution gradually weakened from 50.8 to 24.9%. From a spatial perspective, four and five pollution sources were identified in the upstream and downstream, respectively. Therefore, even though there are some limitations due to the data availability of water monitory and hydrology data, the proposed research framework of this study can be applied to the water environmental management of other similar watersheds.

A New Air-Assisted Flare Tip Design for Managing Gas Flare Emissions (CFD Analysis)

Recently, flares have been considered as a major source of air pollution from the petroleum refining industry. The United Nations has instigated an international effort related to the management of flare emissions to reduce the global warming impact related to flaring. Eliminating or removing the need for gas flares is difficult because these devices are generally used as safety devices to allow the combustion of flammable gases in a controlled fashion which supports safe operation. However, reducing flaring is generally possible using well-designed, efficiently operated flare equipment. In general, flare performance can be enhanced following the API-521 methodology and using assist-media including air and steam to achieve smokeless operation. This present work will discuss flare emissions in the petroleum refining industry and a method to manage flare emissions. Moreover, this work will discuss flare combustion efficiency (CE) and destruction and removal efficiency (DRE) in terms of efficient flare operation. This work uses actual operating flare data, published previously, which will be used in this work together with the CFD Code C3d. This code, developed at the USDOE Sandia National Laboratory, is based on a standard LES methodology to conduct transient flare analysis and is used to simulate flare operation to estimate flame shape and emissions produced. In this work, a new air-assisted flare tip design which uses the Coanda effect to improve flare operation was analyzed. This new flare design reduces the emission rate and demonstrates the design’s effectiveness. The analysis considers a flare 39″ high and 6″ diameter in the center of a 4m x 4m x 4m domain. Boundary conditions assume no cross wind and an ambient temperature of 300 K. The initial condition is a hydrostatic pressure profile across the computational domain. In the air-assist simulation, stoichiometric ratio will be a variable, and therefore, more than one case was considered.

Transcriptome analysis provides new insight into the mechanism of Bombyx mori under zinc exposure

Zinc is a significant source of heavy metal pollution that poses risks to both human health and biodiversity. Excessive concentrations of zinc can hinder the growth and development of insects and trigger cell death through oxidative damage. The midgut is the main organ affected by exposure to heavy metals. The silkworm, a prominent insect species belonging to the Lepidoptera class and widely used in China, serves as a model for studying the genetic response to heavy metal stress. In this study, high-throughput sequencing technology was employed to investigate detoxification-related genes in the midgut that are induced by zinc exposure. A total of 11,320 unigenes and 14,723 transcripts were identified, with 553 differentially expressed genes (DEGs) detected, among which 394 were up-regulated and 159 were down-regulated. The Gene Ontology (GO) analysis revealed that 452 DEGs were involved in 18 biological process subclasses, 14 cellular component subclasses and 8 molecular functional subclasses. Furthermore, the KEGG analysis demonstrated enrichment in pathways such as Protein digestion, absorption and Lysosome. Validation of the expression levels of 9 detoxification-related DEGs through qRT-PCR confirmed the accuracy of the RNA-seq results. This study not only contributes new insights into the detoxification mechanisms mechanism of silkworms against zinc contamination, but also serves as a foundation basis for understanding the molecular detoxification processes in lepidopteran insects.

Tracing nitrogen and phosphorus pollution in urban runoff: Insights from isotopic tracers and SWMM modeling

Water contamination in certain rivers during rainfall necessitates the evaluation of pollutant levels within pipelines and the identification of their sources to ensure effective pollution control. In this study, we assess pollutant concentrations during runoff from surfaces, pipelines, and channels, identify pollutant sources within pipelines, and analyze sewage leakage contributions to channel pollution in residential areas in Wuxi City, China. The results showed that the event mean concentrations of total nitrogen (TN), total phosphorus (TP), ammonium (NH4+-N), and nitrate (NO3−-N) in the drainage outlet runoff were 3.4, 1.1, 8.8, and 14.9 times higher than in impervious surface runoff. The predominant forms of nitrogen and phosphorus in runoff shift from organic to inorganic as they move from the surface to pipelines. The pipeline load and contribution were further estimated by combining water quantity calculations from the calibrated and verified Storm Water Management Model (SWMM) with measured nitrogen and phosphorus concentrations in the runoff. TN, NH4+-N, NO3−-N, and PO43--P within the pipelines accounted for 59%, 83%, 90%, and 77% of the discharge loads, respectively. The application of the Stable Isotope Analysis in R (SIAR) package (MixSIAR) revealed that the domestic sewage and soil organic N were the predominant sources of pollutants within drainage pipelines, contributing 23.7% and 32.6% of NO3−-N, respectively. Additionally, the sewage leakage accounted for 33.7% and 66.9% of NO3−-N in drainage runoff and channels, respectively. Unlike existing methods, this study quantitatively highlights the significant impact of pipeline pollutants and sewage leakage on urban river water quality, providing essential insights for developing effective strategies to mitigate non-point source water pollution.

Quantifying the potential health risk of surface waters in the Qinling giant panda habitat

The giant panda (Ailuropoda melanoleuca) is recognized worldwide as an icon for wildlife conservation. The Qinling subspecies (Ailuropoda melanoleuca qinlingensis) inhabiting the Qinling Mountains is more endangered. Previous studies have indicated that Qinling pandas are potentially at risk from environmental pollutants, which they are exposed to via food and drinking water. However, there is little information about the surface water quality in the Qinling Mountains, and it is unknown whether drinking water is an important pollutant source for pandas. Water samples were collected from five different nature reserves in Qinling, each of which is home to a population of pandas. The samples were analyzed for five essential metals of copper (Cu), zinc (Zn), manganese (Mn), chromium (Cr), and nickel (Ni) and four non-essential metals of lead (Pb), cadmium (Cd), arsenic (As), and mercury (Hg). The concentrations of all heavy metals (HMs) were higher in buffer zones than in the core areas and were highest in Foping and Niuweihe nature reserves. The concentrations of mercury exceeded the water quality standard in the core zone of three reserves, suggesting that NWH (Niuweihe), THS (Tianhuashan), and PHL (Pingheliang) giant panda populations were at risk from mercury toxicity. The accumulation of other elements over time could also pose a serious risk to pandas. Three main sources of pollution were identified: coal combustion, waste incineration, and fertilizer use; traffic-related activities; and metal mining. Environmental pollution is compromising the efforts to conserve the giant panda, and measures need to be put in place to control pollution sources.

Characterizing organophosphate esters and chlorinated paraffins in surface soils affected by diverse e-waste disassembling process in South China: Occurrence, distinct emission, and risk assessment

E-waste recycling activities are a crucial emission source of organic pollutants, posing potential risks to the surrounding environment and human health. To understand the potential impact related to diverse e-waste dismantling activities, we investigated two categories of popular flame retardants (i.e., organophosphate esters (OPEs) and chlorinated paraffins (CPs) and their resultant possible ecological risk in 53 surface soil samples from Qingyuan, a well-known e-waste recycling region in South China. Varied concentrations of ΣOPEs (20.5–8720 ng/g) and ΣCPs (920–16800 ng/g) were observed at diverse dismantling sites, while relatively low levels of ΣOPEs (6.13–1240 ng/g) and ΣCPs (14.8–2870 ng/g) were found in surrounding soils. These results indicated that primitive e-waste dismantling processes were the primary emission source of OPEs and CPs in the studied area, with e-waste dumping and manual dismantling being the most important emission sources for OPEs and CPs. More importantly, CPs could be degraded/transformed into more toxic intermediates via dechlorination and decarbonization during the burning of e-waste. Furthermore, our results indicated the potential ecological risks posed by OPEs and CPs related to e-waste recycling.

Estimating erosion vulnerability within agricultural fields by downscaling the Daily Erosion Project (DEP): the OFEtool

AbstractAgriculture continues to be one of the most important sources of nonpoint source pollution to surface water bodies. Consequently, it is critical to identify and prioritize high‐contributing agricultural fields and sub‐field areas for reducing soil erosion and sediment delivery by implementing best management practices (BMPs). Current erosion risk assessment tools are either complex modelling approaches or rely on a simplified reality and generalized assumption. The Daily Erosion Project (DEP) is a daily estimator of precipitation, hillslope runoff, detachment and soil loss covering ~630 000 km2 across the Midwest United States. These estimations are reported daily and publicly at the hydrologic unit code 12 watershed resolution (approximately 100 km2). The main objective of this study was to develop a new tool (named Overland Flow Element tool [OFEtool]) that downscales the watershed scale of DEP to estimate average runoff and soil displacement within a field, helping to locate erosive hotspots at multiple scales. We also demonstrated the applicability of OFEtool in Bennet Creek‐Sugar Creek in East Central Iowa (the United States) and compared its results with other erosion vulnerability tools such as the Soil Vulnerability Index for Cultivated Cropland (SVI‐cc) and a GIS‐based Revised Universal Soil Loss Equation (RUSLE). The same erosion risk classes and ranges (low, moderate, moderately high and high) were implemented for all indexes. The advantages of the OFEtool compared to the SVI‐cc and RUSLE models are related to the use of an event‐based modelling approach, such as DEP, with updated soil loss estimates based on temporal changes in climate inputs and land use and management. The OFEtool uses a 6‐year time frame and a more up‐to‐date field inputs, while RUSLE provides a long‐term average and SVI‐cc only considers soil and topographical factors for risk assessment. Results indicated that the spatial distribution of vulnerable fields (and parts of the fields) followed a similar trend as other tested indices. However, the risk level associated with each tool differed (SVI‐cc &gt; RUSLE &gt; OFEtool). These differences could arise from intrinsic disparities within the tools (inputs, timing, processes considered, assumptions). While currently limited to the DEP domain and relying on the DEP random sampling scheme, further research is warranted to validate the tool at other Midwest locations and ensure it captures the watershed's landscape variability (combination of terrain, soil, land use and management) required to identifying critical erosion hotspots.

Development of an Aerosol Assimilation System Using a Global Non‐Hydrostatic Model, a 2‐Dimensional Variational Method, and Multiple Satellite‐Based Aerosol Products

AbstractThe computational balance between the model grid resolution and the complexity of the data assimilation technique is essential for accurate aerosol forecasting and obtaining aerosol reanalysis data sets. This study aimed to develop a high‐resolution aerosol assimilation system. A 2‐dimensional variational method (2DVar) was implemented in a non‐hydrostatic icosahedral atmospheric model (NICAM). This new model (NICAM/2DVar), with a global grid size of 56 km, assimilated the observed aerosol optical depth (AOD) that is estimated by combining multiple products of geostationary and polar‐orbital satellites. The model results were evaluated against ground‐based AOD observations on a global scale. They exhibited higher correlations, lower uncertainties, and lower biases than those obtained without the 2DVar. The model also reproduced the observed surface aerosols (PM2.5) mass concentrations, especially in Kyushu, Japan. This occurred because the satellite‐estimated AODs over ocean close to air pollution sources were obtained for many occasions. The correlation coefficient values against the PM2.5 observations increased from 0.44 to 0.65 compared to the results without the 2DVar. The impact of the 2DVar on the forecast results was investigated, and the forecast values for 2–3 days were improved. Because satellite‐retrieved AODs are often lacking over land owing to retrieval difficulties, the use of ground‐based AODs in assimilations is essential for precise processing the of aerosol reanalysis data sets. The computational cost with the use of the 2DVar was only 0.6% more than that without its use. Thus, aerosol assimilation using the NICAM/2DVar can be realistically extended to finer grid sizes.