Waste residues and acidic effluents (post-processing of E-waste) released into the local surroundings cause perilous environmental threats and potential risks to human health. Only limited research and information are available toward the sustainable management of waste residues generated post resource recovery of E-waste components. In the present study, the manual processing of obsolete computer (keyboard, monitor, CPU, and mouse) and chemical leaching of waste printed circuit boards (WPCBs) (motherboard, hard drive, DVD drive, and power supply) were performed for urban mining. The toxicity characteristics of typical pollutants in the residues of the WPCBs (post chemical leaching) were studied by toxicity characteristics leaching procedure (TCLP) test. Manual dismantling techniques resulted in an efficient urban mining concept with an overall average profit estimation of INR 2513.73/US$ 34.59. The chemical leaching of WPCBs showed a high concentration of metal leaching like Cu (229662 ± 575.3 mg/kg) and Pb (36785.67 ± 13.07 mg/kg) in the motherboard after stripping epoxy coating. The toxicity test revealed that the concentration of Cu (245.746 ± 0.016 mg/l) in the treated waste residue and Cu (430.746 ± 0.0015 mg/l) and Pb (182.09 ± 0.0035 mg/l) in the non-treated waste residue exceeded the threshold limit. The concentrations of other elements As, Cd, Co, Cr, Ag, Mn, Zn, Ni, Fe, Se, and In were within the permissible limit. Hence, the waste residue stands non-hazardous except Cu and Pb. Stripping out the epoxy coating of WPCBs enhances the metal leaching concentrations. The study highlighted that efficient and appropriate E-waste urban mining has immense potential in tracing the waste scrap into secondary resources. This study also emphasized that the final processed waste residue (left unattended or discarded due to lack of appropriate skill and technology) can be taken into consideration and exploited for value-added materials.

Large amounts of wastewater discharge have emerged as a burden in the process of industrialization and urbanization. In this study, a dynamic wastewater-induced input-output model is developed to systematically analyze the related situation. The developed model is applied to Guangdong Province, China to analyze its prominent characteristics from 2002 to 2015. Combining input-output analysis, ecological network analysis and structural decomposition analysis, the developed model reveals issues of direct and indirect discharges, relationships among various discharges, and driving forces of wastewater discharges. It is uncovered that Primary Manufacturing and Advanced Manufacturing dominate the system because of significant temporal and spatial variations in wastewater discharge. In addition, Manufacturing of paper, computer and machinery and Services are the key industries responsible for large amounts of wastewater discharge and unhealthy source-discharge relationships. The largest wastewater discharge occurred in 2005 and indirect wastewater discharge is the main form. Furthermore, final demand is found to be the biggest driving force of wastewater discharge. Finally, a three-phase policy implementation system implemented in stages proposes solutions to wastewater problems.

Organophosphate esters (OPEs) and phthalate esters (PAEs) are extensively used as additives in commercial and household products. However, knowledge on human exposure to OPEs and PAEs remains limited in China. This study aimed to investigate OPE and PAE metabolites in urine samples of primiparas and to evaluate the cumulative risk of OPE and PAE exposure. A total of 8 OPE metabolites and 11 PAE metabolites were measured in urine samples of 84 primiparas from Shenzhen, China. The OPE metabolites were found in at least 72% of the urine samples with bis(2-chloroethyl) phosphate (BCEP) being the dominant analogue. Among the 11 PAE metabolites, mono-n-butyl phthalate (mBP) was the most abundant analogue and had a median concentration (139 μg/L) greater than those reported in urine samples from other countries and regions. A significant, positive correlation was found between Σ₈OPEMs (the sum of 8 OPE metabolites) and body mass index (BMI). The urinary concentration of Σ₁₁PAEMs (the sum of 11 PAE metabolites) was positively associated with the time of computer using by the primiparas. The estimated daily intakes (EDI) of tris(2-chlorethyl) phosphate (TCEP, the parent chemical of BCEP) and di-n-butyl phthalate (DnBP, the parent chemical of mBP) were determined to be 0.47 and 9.14 μg/kg bw/day, respectively. The 95th percentile EDI values for TCEP and DnBP both exceeded their corresponding reference doses. Twelve and fifty-five percentage of the primiparas were estimated to have HIRfD (hazard index corresponding to reference doses) and HITDI (hazard index corresponding to tolerable daily intake) values exceeding 1 for OPEs and PAEs, respectively, suggesting a relatively high exposure risk.

Hybrid artificial intelligence (AI) models are developed for sediment lead (Pb) prediction in two Bays (i.e., Bramble (BB) and Deception (DB)) stations, Australia. A feature selection (FS) algorithm called extreme gradient boosting (XGBoost) is proposed to abstract the correlated input parameters for the Pb prediction and validated against principal component of analysis (PCA), recursive feature elimination (RFE), and the genetic algorithm (GA). XGBoost model is applied using a grid search strategy (Grid-XGBoost) for predicting Pb and validated against the commonly used AI models, artificial neural network (ANN) and support vector machine (SVM). The input parameter selection approaches redimensioned the 21 parameters into 9–5 parameters without losing their learned information over the models’ training phase. At the BB station, the mean absolute percentage error (MAPE) values (0.06, 0.32, 0.34, and 0.33) were achieved for the XGBoost–SVM, XGBoost–ANN, XGBoost–Grid-XGBoost, and Grid-XGBoost models, respectively. At the DB station, the lowest MAPE values, 0.25 and 0.24, were attained for the XGBoost–Grid-XGBoost and Grid-XGBoost models, respectively. Overall, the proposed hybrid AI models provided a reliable and robust computer aid technology for sediment Pb prediction that contribute to the best knowledge of environmental pollution monitoring and assessment.

Traffic emissions are a complex and variable cocktail of toxic chemicals. They are the major source of atmospheric pollution in the parts of cities where people live, commute and work. Reducing exposure requires information about the distribution and nature of emissions. Spatially and temporally detailed data are required, because both the rate of production and the composition of emissions vary significantly with time of day and with local changes in wind, traffic composition and flow. Increasing computer processing power means that models can accept highly detailed inputs of fleet, fuels and road networks. The state of the science models can simulate the behaviour and emissions of all the individual vehicles on a road network, with resolution of a second and tens of metres. The chemistry of the simulated emissions is also highly resolved, due to consideration of multiple engine processes, fuel evaporation and tyre wear. Good results can be achieved with both commercially available and open source models. The extent of a simulation is usually limited by processing capacity; the accuracy by the quality of traffic data. Recent studies have generated real time, detailed emissions data by using inputs from novel traffic sensing technologies and data from intelligent traffic systems (ITS). Increasingly, detailed pollution data is being combined with spatially resolved demographic or epidemiological data for targeted risk analyses.

To investigate the association and effects of air pollution level on male semen quality in urban and rural areas, this study examines the outdoor concentrations of particulate matter (PM10), sulfur dioxide (SO2), nitrous dioxide (NO2) and semen quality outcomes for 1346 volunteers in both urban and rural areas in Chongqing, China. We found the urban area has a higher pollution level than the rural area, contrasted with better semen quality in the rural residents, especially for sperm morphology and computer assistant semen analysis (CASA) motility parameters. A multivariate linear regression analysis demonstrates that concentrations of PM10, SO2, and NO2 significantly and negatively are associated with normal sperm morphology percentage (P < 0.001) and sperm kinetic parameters. In conclusion, exposure to higher concentrations of PM10, SO2, and NO2 in urban ambient air may account for worse semen quality in urban males.

Cell phone use and radio-frequency electromagnetic radiation (RF-EMF) are rapidly increasing and may be associated with lower semen quality, yet results from epidemiological studies are inconclusive. Information on electronic devices use was collected through standard questionnaires from 1454 men aged 22–45 years old. Semen volume, sperm concentration, total sperm count, total motility, progressive motility, and normal morphology in repeated specimens were determined by trained clinical technicians. Percent changes [95% confidence intervals (CIs)] were estimated as (10ᵝ−1) × 100 for electronic devices use associated with repeated sperm quality parameters in the linear mixed-effect models. After adjusting for multiple confounders, we found significant inverse associations of total duration of electronic devices use with sperm progressive motility and total motility, duration of cell phone and computer use with sperm concentration, progressive motility, and total motility (all P < 0.05). No significant association was found between cell phone/computer use alone and sperm quality parameters. Moreover, per hour increase of time spent on cell phone talking was associated with decreased sperm concentration and total count by an average of −8.0% (95% CI: −15.2%, −0.2%) and −12.7% (95% CI: −21.3%, −3.1%), respectively. Besides, daily calling time was associated with lower sperm progressive motility and total motility among those who used headsets during a call (P for interaction <0.05). In conclusion, our study suggested that more time spent on electronic devices use had a modest reduction effect on semen quality. Daily calling time was significantly associated with lower sperm concentration and total count, and using headsets during a call appeared to aggravate the negative association between daily calling time and sperm motility. Additional studies are needed to confirm these findings.

Exposure of aquatic organisms to micro- and nano-sized plastic debris in their environment has become an alarming concern. Besides having a number of potentially harmful impacts for individual organisms, plastic particles can also influence the phenotype and performance of their offspring. We tested whether the sperm pre-fertilization exposure to nanoplastic particles could affect offspring survival, size, and swimming performance in the European whitefish Coregonus lavaretus. We exposed sperm of ten whitefish males to three concentrations (0, 100 and 10 000 pcs spermatozoa⁻¹) of 50 nm carboxyl-coated polystyrene spheres, recorded sperm motility parameters using computer assisted sperm analysis (CASA) and then fertilized the eggs of five females in all possible male-female combinations. Finally, we studied embryonic mortality, hatching time, size, and post-hatching swimming performance of the offspring. We found that highest concentration of plastic particles decreased sperm motility and offspring hatching time. Furthermore, sperm exposure to highest concentration of plastics reduced offspring body mass and impaired their swimming ability. This suggests that sperm pre-fertilization exposure to plastic pollution may decrease male fertilization potential and have important transgenerational impacts for offspring phenotype and performance. Our findings indicate that nanoplastics pollution may have significant ecological and evolutionary consequences in aquatic ecosystems.

The adverse health effects resulting from exposure to contaminated soil on internally displaced populations in Mitrovica, Kosovo can be determined by how the potentially harmful elements are bound in the soils. Certainly this was the case for Pb, present at concentrations ranging from 624 to 46,900 mg/kg, and at bioaccessibilities ranging <5% to nearly 90%. To assess why the soil Pb might differ so markedly in terms of its bioaccessibility, computer controlled scanning electron microscopy (CCSEM) was employed to determine how the Pb was associated with other elements at the individual particle (IP) level in soils from the area. It was found that the Pb-bearing particle types were, for the most part, different in each sample. We consider these differences as the main control on Pb bioaccessibility in these soils. Pb solubility at the IP level was evaluated by examining Pb-particles from these soils in the electron microscope before and after successive immersions in a simulated gastric fluid. This analysis (differential IP analysis) confirmed the CCSEM characterization that Pb associated with other higher atomic number elements (Fe, Zn, Cu and Ni) was less soluble than when it was present as isolated phases (e.g., as carbonate) or when it was bound with lower atomic number elements (Na, Al, Si, K, Ca). The heterogeneity in solubility and composition of the Pb-particles suggested that the Pb originated from a range of different anthropogenic activities. The nature of these different anthropogenic activities created the wide differences in Pb-bioaccessibilty by producing Pb bound in many different forms in the soil particles. This type of Pb-particle characterization highlights the role CCSEM analysis, and IP acid extraction, can play in providing supporting evidence alongside bioaccessibility data for applications in human health risk assessment and management of contaminated soil.

This study investigated the occurrence of 8 polybrominated diphenyl ether (PBDE) congeners from homes (n = 20), offices (n = 20), air conditioners (n = 6), and computers (n = 6). High detection frequencies for most of the congeners were observed, indicating continued widespread use of Penta-, Octa- and Deca-BDE mixtures. The median concentrations of ∑PBDEs were 119 and 194 pg m−3 for home air and office air, respectively. Regarding dust, the median concentrations of ∑PBDEs were 239 and 437 ng g−1 for home and office dust, respectively. The ratios of the median concentrations of BDE-209 to ∑PBDEs were approximately 0.95 and 0.87 for home dust and office dust, respectively. The median concentrations of ∑PBDEs were 359 ng g−1 and 350 ng g−1 for dust on air conditioner filters and the back cabinet of the computer, respectively. The ratios of the median concentrations of BDE-209 to ∑PBDEs were approximately 0.58 and 0.46 for air conditioner and computer samples. Running air conditioners contributed to ΣPBDEs in office air through direct and indirect pathways. The daily intake of PBDEs was estimated to be 2630 pg (kg bw)−1 day−1 for toddlers in homes and 319 pg (kg bw)−1 day−1 for adults in homes and offices.