Worst Pollution Research Articles

Synthesizing Jakarta City’s Local Wisdom: Reimagining Sidewalks with an Emotional Design and Sustainability Approach

The research objective was to design sidewalks as walking facilities and public spaces, considering comfort, safety, cleanliness, and functionality for Jakarta’s residents. The city of Jakarta, as a megapolitan center, hosted various societal activities. The high mobility of Jakarta’s population led to busy sidewalk activities, especially walking, regulated by the Director General of Highways’ guidelines. Walking could help reduce pollution, with Jakarta having the worst pollution in 2023. The research problem, based on 360-degree Google Maps images of sidewalks, was that sidewalks serve not only as footpaths but also as public gathering spaces. The qualitative research method examined digital data on sidewalk use in Jakarta (west, central, south, east, and north). The design process incorporated emotional design (visceral, behavioral, reflective) and three dimensions of sustainability (The Overlapping Spheres Model of Triple Bottom Line/TBL). The result is a 3D sidewalk model using emotional and sustainability design approaches. Synthesis of emotional design interprets visceral (sensory), behavioral (habits), and reflective (values) levels. Synthesis of sustainability includes environmental, social, and economic dimensions, focusing on air quality, water management, and energy efficiency. Social and economic aspects aim to meet Jakarta residents’ basic needs. Ultimately, these recommendations aim to make cities and neighborhoods inclusive, safe, resilient, and sustainable. However, the research is limited to design recommendations without direct implementation. Further research or implementation is needed to address these challenges and ensure Jakarta residents’ future well-being.

Telerehabilitation of Chronic Obstructive Pulmonary Disease Patients in a Tertiary Care Hospital in Western India: A 3-month Follow-up Study

Abstract Introduction: India grapples with a mounting chronic obstructive pulmonary disease (COPD) crisis driven by pervasive air pollution, especially from biomass burning. New Delhi’s classification as the capital with the worst pollution, with 35 other Indian cities in the global top 50, underscores the severity. COPD, the third-leading cause of global mortality, claimed 3.23 million lives in 2019, with a projected surge due to prevalent smoking and ongoing pollution. Domestically, COPD accounts for 9.5% of all deaths. Emphasizing the urgency of Sustainable Development Goal 3.4 to reduce mortality from non-communicable diseases, including chronic respiratory diseases, cost-effective pulmonary rehabilitation (PR), particularly telerehabilitation, emerges as a promising avenue for comprehensive COPD management in this challenging landscape. Methods: Patients with COPD (gold stages 1–3) confirmed by pulmonary function tests were enrolled. Functional exercise capacity, dyspnoea levels and health-related quality of life (HRQoL) were assessed using the 6-min walk test (6MWD), modified Medical Research Council (mMRC) dyspnoea scale and SGRQ-C questionnaire, respectively. Participants received home-based PR and attended one 2-h PR session 3 days a week for 12 weeks. Follow-up was conducted telephonically monthly, and a final assessment was done at the end of the 3rd month. Results: Of the 88 initially recruited patients, 26 were lost to follow-up, leaving 62 who completed the 3-month programme. Significant improvements were observed in exercise tolerance (6-min walk test [6MWD]), with mean values increasing from 383.00 ± 71.22 to 441.3 ± 87.3 (P < 0.001). HRQoL, assessed by SGRQ-C, showed an overall improvement, with statistically significant reductions in total scores (42.15 ± 3.52–39.54 ± 3.41, P < 0.001) and in each domain (symptom, impact and activity). Conclusion: The study affirms telerehabilitation’s efficacy in enhancing exercise capacity and improving the quality of life for COPD patients, advocating its integration, particularly in regions with limited access to conventional rehabilitation programmes.

The preparation of spherical Dopamine/Carbon for the efficient removal of uranium

As Japan releases nuclear wastewater into the ocean, radioactive contamination has caused global concern. Among those radioactive contaminants, uranium(U) is one of the worst pollutants. This study prepares the spherical dopamine/carbon(DA/C) through a simple hydrothermal process to remove U(VI) from water. In adsorption, uranium ions are adsorbed on the material surface and gradually enter the material through the pores. U(VI) adsorption is a single molecule adsorption process, and the adsorption capacity of the DA/C is 30.4 mg/g at pH = 7.0 and 288 K. U(VI) adsorption depends on electrostatic attraction and complexation. The hydroxyl groups and amino groups of the DA/C can form complexes with U(VI) ions. The DA/C possesses excellent desorption and regeneration ability, which can be used for U(VI) removal and extract uranium from seawater. This study provides a new idea for enriching U(VI), which helps control radioactive pollution.

Spatiotemporal Variations of Aerosol Optical Depth and the Spatial Heterogeneity Relationship of Potential Factors Based on the Multi-Scale Geographically Weighted Regression Model in Chinese National-Level Urban Agglomerations

Investigating the spatiotemporal variation characteristics of aerosol optical depth (AOD) and its driving factors is essential for assessing atmospheric environmental quality and alleviating air pollution. Based on a 22-year high-resolution AOD dataset, the spatiotemporal variations of AOD in mainland China and ten national urban agglomerations were explored based on the Mann–Kendall trend test and Theil–Sen median method. Random forest (RF) and multiscale geographically weighted regression (MGWR) were combined to identify the main driving factors of AOD in urban agglomerations and to reveal the spatial heterogeneity of influencing factors. The results showed that areas with high annual average AOD concentrations were mainly concentrated in the Chengdu–Chongqing, Central Plains, Shandong Peninsula, and Middle Yangtze River urban agglomerations. Southern Beijing–Tianjin–Hebei and its surrounding areas revealed the highest AOD pollution during summer, whereas the worst pollution during the remaining three seasons occurred in the Chengdu–Chongqing urban agglomeration. Temporally, except for the Ha-Chang and Mid-Southern Liaoning urban agglomerations, where the average annual AOD increased, the other urban agglomerations showed a decreasing trend. Among them, the Central Plains, Middle Yangtze River, Guanzhong Plain, and Yangtze River Delta urban agglomerations all exhibited a decline greater than 20%. According to the spatial trends, most urban agglomerations encompassed much larger areas of decreasing AOD values than areas of increasing AOD values, indicating that the air quality in most areas has recently improved. RF analysis revealed that PM2.5 was the dominant factor in most urban clusters, followed by meteorological factors. MGWR results show that the influencing factors have different spatial scale effects on AOD in urban agglomerations. The socioeconomic factors and PM2.5 showed strong spatial non-stationarity with regard to the spatial distribution of AOD. This study can provide a comprehensive understanding of AOD differences among urban agglomerations, and it has important theoretical and practical implications for improving the ecological environment and promoting sustainable development.

Critical success factors for construction industry transition to circular economy: developing countries’ perspectives

PurposeCircular economy (CE) is increasingly used as a system solution framework for tackling climate change. Existing CE solutions have been found effective in other sectors but ineffective in the construction sector (CS) due to the inability to account for unique CS dynamics that are essential for its success. With CS being one of the worst polluters, this research aimed to investigate critical success factors (CSFs) and a practical CE implementation framework for the sector.Design/methodology/approachMixed methods research involving descriptive surveys of construction professionals in Kazakhstan, Malaysia and Turkey was used to explore priority dimensions of McKinsey’s ReSOLVE (“regenerate, share, optimize, loop, virtualize and exchange”) circulatory framework, and the associated CSFs that underpin successful implementation of CE in the sector. Empirical data were analyzed using the fuzzy analytical hierarchy process (F-AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) algorithm.FindingsResults showed that “Optimize” and “Loop” were the most critical of the six dimensions of CE implementation framework for successful circularity transition of the CS in each case study country. Twenty-seven CSFs identified in the study were clustered into seven key action areas for successful rollout of the ReSOLVE framework, namely, legislative and documentation, financial, design, external/stakeholder, technological, internal and construction/production/waste management.Practical implicationsThe study makes a unique contribution to existing knowledge by exploring ReSOLVE-based CSFs for successful CE implementation in the CS through the lenses of experienced construction practitioners and experts in developing countries. The findings are expected to provide a deeper insight into the most significant CSFs to be focused on with the limited resources available to decision-makers in the CS. The findings would also inform regulatory policy reformations aimed at facilitating greater rate of implementation of CE in the construction value chain.Originality/valueThe study – the first of its kind – established and validated a wholistic and construction-specific CE implementation framework to guide built environment professionals and policymakers in formulating a roadmap for successful CS’s transition to circularity and hopefully paving the way for improved economic, social and environmental performance of the sector.

Determination of cytotoxicity following oxidative treatment of pharmaceutical residues in wastewater

Pharmaceutical residues are released in the aquatic environment due to incomplete removal from wastewater. With the presence of multiple chemicals in sewage waters, contaminants may adversely affect the effectiveness of a wastewater treatment plant (WWTP). In certain cases, discharged metabolites are transformed back into their pristine structure and become bioactive again. Other compounds are persistent and can withstand conventional wastewater treatment. When WWTP effluents are released in surface waters, pristine and persistent chemicals can affect the aquatic environment. To complement WWTPs and circumvent incomplete removal of unwanted chemicals or pharmaceuticals, on-site wastewater treatment can contribute to their removal. Advanced oxidation processes (AOPs) are very powerful techniques for the abatement of pharmaceuticals, however, under certain circumstances reactive toxic by-products can be produced. We studied the application of on-site AOPs in a laboratory setting. It is expected that treatment at the contamination source can eliminate the worst polluters. Thermal plasma and UV/H2O2 oxidation were applied on simulation matrices, Milli-Q and synthetic sewage water spiked with 10 different pharmaceuticals in a range of 0.1 up to 2400μg/L. In addition, untreated end-of-pipe hospital effluent was also subjected to oxidative treatment. The matrices were activated for 180min and added to cultured HeLa cells. The cells were 24h and 48h exposed at 37°C and subsequently markers for oxidative stress and viability were measured. During the UV/H2O2 treatment periods no toxicity was observed. After thermal plasma activation of Milli-Q water (150 and 180min) toxicity was observed. Direct application of thermal plasma treatment in hospital sewage water caused elimination of toxic substances. The low cytotoxicity of treated pharmaceutical residues is likely to become negligible if plasma pre-treated on-site wastewater is further diluted with other sewage water streams, before reaching the WWTP. Our study suggests that AOPs may be promising technologies to remove a substantial portion of pharmaceutical components by degradation at the source. Further studies will have to be performed to verify the feasibility of upscaling this technology from the benchtop to practice.

Health impact assessment of air pollution in Lisbon, Portugal

ABSTRACT Background: Lisbon has about 500,000 inhabitants and it’s the capital and the main economic hub of Portugal. Studies have demonstrated that exposure to Particulate Matter with an aerodynamic diameter<2.5 μm (PM2.5) have strong association with health effects. Researchers continue to identify new harmful air pollutants effects in our health even in low levels. Objectives: This study evaluates air pollution scenarios considering a Health Impact Assessment approach in Lisbon, Portugal. Methods: We have studied abatement scenarios of PM2.5 concentrations and the health effects in the period from 2015 to 2017 using the APHEKOM tool and the associated health costs were assessed by Value of Life Year. Results: The mean concentration of PM2.5 in Lisbon was 23 μg/m3 ± 10 μg/m3 (±Standard Deviation). If we consider that World Health Organization (WHO) standards of PM2.5 (10 μg/m3) were reached, Lisbon would avoid more than 423 premature deaths (equivalent to 9,172 life years’ gain) and save more than US$45 million annually. If Lisbon city could even diminish the mean of PM2.5 by 5 μg/m3, nearly 165 deaths would be avoided, resulting in a gain of US$17 million annually. Conclusion: According to our findings, if considered the worst pollution scenario, levels of PM2.5 could improve the life’s quality and save a significant amount of economic resources. Implications: The manuscript addresses the health effects and costs of air pollution and constitutes an important target for improving public policies on air pollutants in Portugal. Although Portugal has low levels of air pollution, there are significant health and economic effects that, for the most part, are underreported. The health impact assessment approach associated with costs had not yet been addressed in Portugal, which makes this study more relevant in the analysis of policies aimed to drive stricter control on pollutants’ emissions. Health costs are a fundamental element to support decision-making process and to orientate the trade-offs in investments for improving public policies so that to diminish health effects, which can impact the management of the local health services and the population’s quality of life, especially after the pandemic period when resources are scarce.

Neurotoxicity and the Global Worst Pollutants: Astroglial Involvement in Arsenic, Lead, and Mercury Intoxication.

Environmental pollution is a global threat and represents a strong risk factor for human health. It is estimated that pollution causes about 9 million premature deaths every year. Pollutants that can cross the blood-brain barrier and reach the central nervous system are of special concern, because of their potential to cause neurological and development disorders. Arsenic, lead and mercury are usually ranked as the top three in priority lists of regulatory agencies. Against xenobiotics, astrocytes are recognised as the first line of defence in the CNS, being involved in virtually all brain functions, contributing to homeostasis maintenance. Here, we discuss the current knowledge on the astroglial involvement in the neurotoxicity induced by these pollutants. Beginning by the main toxicokinetic characteristics, this review also highlights the several astrocytic mechanisms affected by these pollutants, involving redox system, neurotransmitter and glucose metabolism, and cytokine production/release, among others. Understanding how these alterations lead to neurological disturbances (including impaired memory, deficits in executive functions, and motor and visual disfunctions), by revisiting the current knowledge is essential for future research and development of therapies and prevention strategies.

Chemical fingerprinting of organic micropollutants in different industrial treated wastewater effluents and their effluent-receiving river

Industry wastewater is considered one of the worst polluters of our precious water ecologies. However, the types of pollutants present in wastewater from industrial wastewater treatment plants (IWTPs) are still unclear. In this study, a simple and effective chemical fingerprinting method for checking the source-sink relationships among different industrial wastewaters and their effluent-receiving river was established. 107, 228, 155, and 337 chemicals were screened out in wastewater from electronics, steel, textile, and printing and dyeing plants, respectively. Chemical fingerprinting of the detected chemicals was performed, and results showed that aromatic compounds were the most prevalent among the pollutant categories (i.e., 56, 189, and 168 in electronics, iron and steel, and printing and dyeing plants, respectively). The traceability analysis of the chemicals selected in the effluent determined the characteristic pollutants of different industrial enterprises. Sixty-eight compounds were identified as the characteristic pollutants in the different process stages of wastewater of the four IWTPs. Of the 84 effluent-receiving river water signature pollutants, 47.6% (n = 40) were also detected in the effluent from the four IWTPs. Effective screening of organic pollutants in industrial wastewater and determining their sources will help accelerate the improvement of industrial wastewater treatment technology.

Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

In the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

EUROPEAN GREEN DEAL AND THE NEW POLICY GOALS IN TRANSPORT AND MOBILITY - HOW GAMIFICATION CAN INFLUENCE PRO-ENVIRONMENTAL BEHAVIOUR FOR CUTTING CARBON EMISSIONS IN THE EU

Road traffic is one of the major sources of many of the worst pollutants, including carbon dioxide (CO2), carbon monoxide, carcinogenic particles and noise. The past decades have seen a dramatic rise in these harmful effects on human health, that proved to cause at the same time massive damage to the natural and built environment. In response to this aggravating situation, the new European Commission’s President Ursula von der Leyen placed as the centrepiece of her political mandate ‘the European Green Deal’, a comprehensive climate and nature package of measures to make Europe climate neutral by 2050. To reach this very ambitious goal, there is a need for very creative and efficient policy solutions. And this is the scope of the current study. As the sustainability concerns become vital for policy planning, the paper advocates for the need of all EU’s main stakeholders to realise the potential of ‘green gamification’ to help EU member states reach these ambitious policy goals, particularly in the field of transport and mobility - which are of crucial importance for overall CO2 reduction. Placed at the intersection of technology innovation and the need to find more efficient ways to protect the environment, ‘green gamification’ is an emerging concept that refers to the usage of game mechanics when it comes to engaging people, with the purpose to change their behaviour on sustainability issues. In short, it aims to motivate a sustainable behaviour within companies, institutions and citizens, with the use of interactive games, in order to fight against pollution and climate change. Applied to the field of transport and mobility, green gamification implies using strategies to cut carbon emissions especially by convincing people to reduce the use of private cars. But how can we better incentivise citizens’ behaviour for cutting carbon emissions and achieve the new EU transport and mobility policy goals for 2050? To tackle this relevant question the article aims to assess the potential of ‘green gamification’ to help EU member states reach these ambitious policy goals.

Policy on Marine Plastic Waste in Asean and Viet Nam

Marine plastic waste is a transboundary issue which requires integrated regional cooperation. Viet Nam is one of Asia’s five worst polluters of ocean plastic waste, ranks 17th in the world for ocean plastic waste pollution. The Vietnamese Government has paid special attention to the issue of marine plastic waste. This article provides an analysis of the Association of Southeast Asian Nation (ASEAN) and Viet Nam policies on marine plastic waste, and the opportunity of Vietnam to solve this problem at the national and regional levels.

Eco-Friendly garment processing using aerosol technology

Textile processing industry is considered as one of the largest consumers and worst polluters of fresh water resources. Since ages, water has been used as a major processing medium for the pretreatment, dyeing and finishing of textiles. Aim of this study was to compare the traditional aqueous textile processing with water-air mixture as the processing medium using aerosol technology. The concept originally developed by Jeanologia for denim processing, has been adapted with appropriate equipment modifications and parameter optimization for the pretreatment, dyeing and finishing of socks at commercial scale at Interloop Industries Pakistan, which is one of the largest socks manufactures in the world. In the new process, a blend of pressurized air with minimal amount of water is used to transfer the dyes and chemicals to the garment surface for subsequent absorption. The results show 75% savings in water consumption, 47% savings in energy, 80% savings in salt and up to 50% savings in other chemicals used in socks processing at commercial scale. The new process has huge potential to reshape the garment processing industry by minimizing its water, chemical and carbon foot print.

Estimation of Idle Emissions from the On-Road Vehicles in Dhaka

Vehicle emission is a major source of air pollution in Dhaka. Old fleet, lack of maintenance, improper traffic and parking management, overloading, fuel adulteration etc. are responsible for high emissions from the vehicle sector. In this study, vehicle emissions have been measured on-road in Dhaka using an Automotive Gas Analyzer and Smoke Opacity Meter to determine the existing vehicle emission scenario in the city. Concentrations of carbon monoxide (CO) and hydrocarbons (HC) in the emissions from CNG/gasoline vehicles, and opacity of the emissions from diesel vehicles were measured. The results were compared with the corresponding national limit values. It was found that all types of CNG vehicles performed very well with more than 80% satisfying the corresponding limit values. Private cars ranked at the top in performance among the CNG/gasoline vehicles. Diesel vehicles were found as the worst polluters in the vehicle sector; emissions from about 75% of the diesel vehicles had opacity more than 65 HSU, the national limit value for emissions from diesel vehicles. Motor cycles were also highly polluting; 60% of the motor cycles emitted CO and HC concentrations higher than the respective national emission limit values.

Possible health impacts due to animal and human fecal pollution in water intended for drinking water supply of Rio de Janeiro, Brazil

AbstractFecal matter is considered as one of the worst pollutants in waterbodies due to the potential spread of waterborne diseases. This study aimed to determine the host-specific fecal contamination in two Brazilian watersheds and to predict the possible impacts on human health. Fecal sources were enumerated using host-specific genetic markers to swine (16S rRNA), human and bovine (archaeal nifH), and equine (archaeal mcrA). A single cycling condition was established for four markers aiming to decrease the analysis time. Fifteen samples from São João watershed (75%) and 25 from Guandu (62.5%) presenting Escherichia coli enumeration in compliance with Brazilian guidelines (&amp;lt;1,000 MPN/100 mL) showed the human marker. Furthermore, the bovine, swine, and equine markers were present in 92% (59/64), 89% (57/64), and 81% (52/64) of the water samples, respectively. The molecular markers proposed for qPCR in our study were sensitivity and specific enough to detect host-specific fecal pollution in all samples regardless of E. coli levels reaffirming the low correlation among them and supporting their use in water quality monitoring programs. To our knowledge, this is the first study using this approach for quantification of nifH, mcrA, and rrs gene-associated human and animal fecal pollution in waters intended for drinking water supply in Brazil.

Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

Water quality of the Indus River around the upper basin and the main river was evaluated with the help of statistical analysis. In order to analyze the similarities and dissimilarities for identifying the spatial variations in water quality of the Indus River and sources of contamination, multivariate statistical analysis, i.e., principle component analysis (PCA), cluster analysis, and descriptive analysis, was done. Data of 8 physicochemical quality parameters from 64 sampling stations belonging to 6 regions (labeled as M1, M2, M3, M4, M5, and M6) were used for analysis. The parameters used for assessing the water quality were pH, dissolved oxygen (DO), oxygen reducing potential (ORP), electrical conductivity (EC), total dissolved solids (TDS), salinity (%), and concentration of arsenic (As) and lead (Pb), respectively. PCA assisted in extracting and recognizing the responsible variation factors of water quality over the region, and the results showed three underlying factors including anthropogenic source pollution along with runoff due to rain and soil erosion were responsible for explaining the 93.87% of total variance. The parameters which were significantly influenced by anthropogenic impact are DO, EC, TDS (negative), and concentration of Pb (positive), while the concentration of As, % salinity, and ORP are affected by erosion and runoff due to rain. The worst pollution situation for regions M1 and M6 was due to the concentration of As which was approximately 400 μg/l (i.e., 40 times higher than minimum WHO recommendation). Furthermore, the results also indicated that, in the Indus River, three monitoring stations and five quality parameters are sufficient to have a reasonable confidence about the quality of water in this most important reserve of Pakistan.

Spatial and Temporal Characteristics of Air Quality and Cause Analysis of Heavy Pollution in Northeast China

Northeastern China experiences severe atmospheric pollution, with an increasing occurrence of heavy haze episodes. Based on ground monitoring data, satellite products and meteorological products of atmospheric pollutants in northeast China from 2013 to 2017, the characteristics of spatial and temporal distribution of air quality and the causes of heavy haze events in northeast China were discussed. It was found that the "Shenyang-Changchun-Harbin" city belt was the most polluted area in the region on an annual scale. The spatial distribution of air quality index (AQI) values had a clear seasonality, with the worst pollution occurring in winter, an approximately oval-shaped polluted area around western Jilin Province in spring, and the best air quality occurring in summer and most of autumn. The three periods that typically experienced intense haze events were Period I from late-October to early-November (i. e., late autumn and early winter), Period Ⅱ from late-December to January (i. e., the coldest time in winter), and Period Ⅲ from April to mid-May (i. e., spring). During Period I, strong PM2.5 emissions from seasonal crop residue burning and coal burning for winter heating were the dominant reasons for the occurrence of extreme haze events (AQI>300). Period Ⅱ had frequent heavy haze events (200 < AQI < 300) in the coldest months of January and February(200 < AQI < 300), which were due to high PM2.5 emissions from coal burning and vehicle fuel consumption, a lower atmospheric boundary layer, and stagnant atmospheric conditions. Haze events in Period Ⅲ, with high PM10 concentrations, were primarily caused by the regional transportation of windblown dust from degraded grassland in central Inner Mongolia and bare soil in western Jilin Province. Local agricultural tilling could also release PM10 and enhance the levels of windblown dust from tilled soil.

Proton Chelating Ligands Drive Improved Chemical Separations for Rhodium.

Current methods for the extraction of rhodium carry the highest carbon footprint and worst pollution metrics of all of the elements used in modern technological applications. Improving upon existing methods is made difficult by the limited understanding of the molecular-level chemistry occurring in extraction processes, particularly in the hydrometallurgical separation step. While many of the precious metals can be separated by solvent extraction, there currently exist no commercial extractants for Rh. This is due to its complicated mixed speciation upon leaching into hydrochloric acid, which gives rise to difficulties in designing effective reagents for solvent extraction. Herein we show that the diamidoamine reagent N- n-hexylbis( N-methyl- N- n-octylethylamide)amine transports Rh(III) from aqueous HCl into an organic phase as the monoaquated dianion [RhCl5(H2O)]2- through the formation of an outer-sphere assembly; this assembly has been characterized by experimentation (slope analysis, FT-IR and NMR spectroscopy, EXAFS, SANS, and ESI-MS) and computational modeling. The paper demonstrates the importance of applying a broad range of techniques to obtain a convincing mode of action for the complex processes involved in anion recognition in the solution phase. A consistent and comprehensive understanding of how the ligand operates to achieve Rh(III) selectivity over the competitor anion Cl- has emerged. This knowledge will guide the design of extractants and thus offers promise for improving the sustainability of metal extraction from both traditional mining sources and the recycling of secondary source materials.

Heavy Particulate Matter Pollution during the 2014-2015 Winter in Tianjin, China

Tianjin, a city located in North China, is heavily polluted with frequent haze, particularly in winter. In this study, continuous online field observations of the sulfate, nitrate, ammonium, PM2.5, and gaseous pollutant concentrations in addition to the meteorological parameters were conducted in Tianjin during the 2014–2015 winter (December–January–February). The PM2.5 concentrations ranged from 5.6 to 495.6 µg m–3, with an average value of 112.2 ± 96.1 µg m–3. The worst pollution was observed in January, when levels on 10% of the days exceeded 250 µg m–3, qualifying as severely polluted, and four haze episodes, each lasting 5–7 days, occurred in rapid succession (separated by ~1-day intervals). Back-trajectory and chemical composition analysis suggested that elevated levels of secondary ionic aerosol species were a primary cause of these episodes. Regional transport of PM2.5 also played a large role in the formation of haze. CO was selected as an inactive chemical tracer in studying the chemical process of PM2.5 formation. The results indicated that when high concentrations of PM2.5 were present, the formation of secondary PM2.5 increased while the photochemical production of O3 ceased. The sulfur oxidation ratio [SOR = nSO42–/(nSO42– + nSO2); n refers to the molar concentration] and nitrate oxidation ratio [NOR = nNO3–/(nNO3– + nNO2)] increased with the PM2.5 level, and heterogeneous processes on the surfaces of fine particles rather than photochemistry drove the haze events. This research elucidates haze formation mechanisms, which must be understood in order to create effective control policies in Tianjin.

Regional Characteristics and Causes of Haze Events in Northeast China

Northeast China experiences severe atmospheric pollution, with an increasing occurrence of heavy haze episodes. However, the underlying forces driving haze formation during different seasons are poorly understood. In this study, we explored the spatio-temporal characteristics and causes of haze events in Northeast China by combining a range of data sources (i.e., ground monitoring, satellite-based products, and meteorological products). It was found that the ‘Shenyang-Changchun-Harbin (SCH)’city belt was the most polluted area in the region on an annual scale. The spatial distribution of air quality index (AQI) values had a clear seasonality, with the worst pollution occurring in winter, an approximately oval-shaped polluted area around western Jilin Province in spring, and the best air quality occurring in summer and most of the autumn. The three periods that typically experienced intense haze events were Period I from mid-October to mid-November (i.e., late autumn and early winter), Period II from late-December to February (i.e., the coldest time in winter), and Period III from April to mid-May (i.e., spring). During Period I, strong PM2.5 emissions from seasonal crop residue burning and coal burning for winter heating were the dominant reasons for the occurrence of extreme haze events (AQI > 300). Period II had frequent heavy haze events (200 < AQI < 300) in the coldest months of January and February, which were due to high PM2.5 emissions from coal burning and vehicle fuel consumption, a lower atmospheric boundary layer, and stagnant atmospheric conditions. Haze events in Period III, with high PM10 concentrations, were primarily caused by the regional transportation of windblown dust from degraded grassland in central Inner Mongolia and bare soil in western Jilin Province. Local agricultural tilling could also release PM10 and enhance the levels of windblown dust from tilled soil. Better control of coal burning, fuel consumption, and crop residue burning in winter and autumn is urgently needed to address the haze problem in Northeast China.