Because of environmental and societal concerns, new strategies are being developed to mitigate the effects of road salt. These include new deicers that are alternatives to or mixtures with the most common road salt, sodium chloride (NaCl), improved techniques and equipment, and biotic mitigation methods. Using outdoor mesocosms, we investigated the impacts of NaCl and two common alternatives, magnesium chloride (MgCl₂) and calcium chloride (CaCl₂) on freshwater communities. We also investigated the mitigation ability of a common macrophyte, Elodea. We hypothesized that road salt exposure reduces filamentous algae, zooplankton, and macrocrustaceans, but results in increases in phytoplankton and gastropods. We also hypothesized that MgCl₂ is the most toxic salt to communities, followed by CaCl₂, and then NaCl. Lastly, we hypothesized that macrophytes mitigate some of the effects of road salt, specifically the effects on primary producers. We found that all three salts reduced filamentous algal biomass and amphipod abundance, but only MgCl₂ reduced Elodea biomass. MgCl₂ had the largest and longest lasting effects on zooplankton, specifically cladocerans and copepods, which resulted in a significant increase in phytoplankton and rotifers. CaCl₂ increased ostracods and decreased snail abundance, but NaCl increased snail abundance. Lastly, while we did not find many interactions between road salt and macrophyte treatments, macrophytes did counteract many of the salt effects on producers, leading to decreased phytoplankton, increased filamentous algae, and altered abiotic responses. Thus, at similar chloride concentrations, NaCl alternatives, specifically MgCl₂, are not safer for aquatic ecosystems and more research is needed to find safer road management strategies to protect freshwater ecosystems.

Although already eliminated in most industrial processes, mercury, as an essential ingredient in all energy-efficient lighting technologies, is still used in fluorescent lamp manufacturing. This study was conducted to investigate the atmospheric pollution caused by fluorescent lamp production and assess the associated public health risk in a large industrial and commercial city of south China, Zhongshan, which is a major production hub of lighting products. Concentrations of total gaseous mercury (TGM) in the atmosphere were measured over a total of 342 sites in the industrial, commercial, and residential areas. The average levels of TGM in the industrial, commercial, and residential areas prior to the landing of a typhoon were 12 ± 11, 3.6 ± 2.1, and 2.7 ± 1.3 ng⋅m⁻³, respectively. TGM concentrations in the industrial areas exhibited significant diurnal variation, with levels in the working hours being much higher than those in the non-working hours, which indicates that the high atmospheric mercury concentrations were contributed by local emissions, instead of regional transport. Most fluorescent lamp manufacturing activities in the city were shut down during a typhoon event, which resulted in a significant reduction in the average TGM level (down to 1.6 ± 1.8 ng⋅m⁻³) and rendered the difference in the average TGM levels in the industrial areas no longer significant between the working and non-working hours. Elevated TGM levels (up to 49 ng⋅m⁻³) were found near clusters of small-scale fluorescent lamp workshops in both industrial and commercial areas, which is indicative of significant emissions of mercury vapor resulting from obsolete equipment and production technologies. No significant non-carcinogenic risk was found for the general residents in the sampling area over the study period, while the risk for the workers in the fluorescent lamp manufacturing facilities and workshops could be higher. These findings indicate that fluorescent lamp manufacturing in the developing countries is a major source of atmospheric mercury.

Based on the archival data on oil facilities, oil spill incidents, and environmental conditions, we researched the plausible causes of oil spill disasters in the Niger Delta of Nigeria between 2006 and 2019. The data were analyzed for geospatial and statistical patterns, using ArcGIS and R programming platforms, respectively. A fuzzy logic algorithm was employed to generate three oil spill disaster models (hazard, vulnerability, and risk). Ordinary Least Square algorithm was adopted to model the relationships between oil spill and two sets of predictor variables: oil facilities (oil well, flow station, and pipeline) and disaster models. We found that, during the 23 years, the Niger Delta experienced 7940 oil spill incidents, of which 67% occurred onshore. A total of 4,950, 501, 855 episodes were attributed to sabotage, corrosion, and equipment failure, with 87%, 62%, and 45% occurring onshore, respectively. Besides, 81% of the 5320 onshore oil spill cases were attributed to sabotage, while corrosion and equipment failure accounted for mere 6% and 7% of the incidents, respectively. The estimated average risk index (R = 0.20) shows that the risk of an oil spill disaster in the Niger Delta is low. Whereas, 5% of the region is characterized by a high risk of oil spill disaster. Furthermore, the regression model infers that the oil spillages exhibit a positive relationship with disaster models and oil facilities at α = 0.10. However, only 16% of the incidents were explained by disaster models, while the oil facilities account for 23% of the total cases, indicating the influence of other factors. To avert further socio-environmental damage in the Niger-Delta, oil theft and sabotage should be curbed, polluted areas are remediated, and an all-inclusive socio-economic development is prioritized.

Pore water is a crucial storage medium and a key source of sediment phosphorus. A novel equipment based on electrokinetic geosynthetics (EKGs) was used for isolating phosphorus from eutrophic lake sediments through pore water drainage. Three mutually independent indoor group experiments (A, B, and C) were conducted to investigate the effects of voltage gradient (0.00, 0.25, and 0.50 V/cm) on pore water drainage capacity, phosphorus removal performance, sediment physicochemical properties, and phosphorus storage dynamics. The average reduction in the sediment moisture and total phosphorus content was 2.5%, 4.3%, and 4.6% and 28.15, 75.95, and 112.65 mg/kg after 6 days of treatment for A, B and C, respectively. Efficient pore water drainage through gravity and electroosmotic flow and electromigration of phosphate were the main drivers of sediment-dissolved and mobilized phosphorus separation. A high voltage gradient facilitated the migration of pore water and the phosphorus in it. The maximal effluent total phosphorous (TP) concentration was up to 27.9 times that in the initial pore water samples, and negligible effluent TP was detected when the pore water pH was less than 2.5. The TP concentration was exponentially and linearly related to the pH and electronic conductivity of the electroosmotic flow, respectively. The migration of H⁺ within the sediment matrix promoted the liberation of metals bounded to phosphorus, particularly of Ca–P and Fe–P. Pore water drainage through an EKG resulted in Ex–P separation of up to 87.50% and a 13.84 mg/kg decrease in Ca–P and 125.35 mg/kg accumulation of low mobile Fe–P in the weak acid anode zone.

The ability of Posidonia oceanica spheroids (egagropiles, EG) to incorporate plastics was investigated along the central Italy coast. Plastics were found in the 52.84% of the egagropiles collected (n = 685). The more represented size of plastics has range within 1–1.5 cm, comparable to the size of natural fibres. Comparing plastics occurring both in EG and in surrounding sand, Polyethylene, Polyester and Nylon were the most abundant polymers in EG, while PSE, PE, PP and PET were the most represented in sand. In particular PE and PP were significantly more represented in sand, while PE, Nylon, Polyester and microfibers (as pills) were more represented in EG. Within plastics found in EG, 26.9% were microfibers as small pills (<1 cm), mainly composed of polyamide, polyester, cotton and PET mixing. These microfibers might be produced by discharges from washing machines and currently represents an emerging pollutant with widespread distribution in marine and freshwater ecosystems.

Measurements of real-world cooking emission factors (CEFs) were rarely reported in recent year's studies. However, the needs for accurately estimating CEFs to produce cooking emission inventories and further implement controlling measures are urgent. In this study, we collected cooking emission aerosols from real-world commercial location operations in Beijing, China. 2 particulate (PM₂.₅, OC) and 2 gaseous (NMHC, OVOCs) CEF species were examined on influencing activity conditions of cuisine type, controlling technology, operation scales (represented by cook stove numbers), air exhausting volume, as well as location and operation period. Measured NMHC emission factors (Non-barbecue: 8.19 ± 9.06 g/h and Barbecue: 35.48 ± 11.98 g/h) were about 2 times higher than PM₂.₅ emission factors (Non-barbecue: 4.88 ± 3.43 g/h and Barbecue: 15.48 ± 7.22 g/h). T-test analysis results showed a significantly higher barbecued type CEFs than non-barbecued cuisines for both particulate and gaseous emission factor species. The efficacy of controlling technology was showing an average of 50 % in decreasing PM₂.₅ CEFs while a 50 % in increasing OC particulate CEFs. The effects of controlling equipment were not significant in removing NMHC and OVOCs exhaust concentrations. CEF variations within cook stove numbers and air exhausting volume also reflected a comprehensive effect of operation scale, cuisine type and control technology. The simulations among activity influencing factors and CEFs were further determined and estimated using hierarchical multiple regression model. The R square of this simulated model for PM₂.₅ CEFs was 0.80 (6.17 × 10–⁹) with standardized regression coefficient of cuisine type, location, sampling period, control technology, cook stove number (N) and N² of 5.18 (0.02), 5.33 (0.02), 1.93 (0.19), 9.29 (4.18 × 10–⁶), 9.10 (1.71 × 10–³) and −1.18 (2.43 × 10–³), respectively. In perspective, our study provides ways of better estimating CEFs in real operation conditions and potentially highlighting much more importance of cooking emissions on air quality and human health.

Solvent use and paint consumption are significant source sectors of volatile organic compounds (VOCs) emissions in China. The occupational painters have high risk of health effect due to exposure to high VOCs concentration. However, the toxic components in coating environment have not been carefully identified, and the health risks of VOCs exposure have not been sufficiently assessed. This study collected air samples from nine workshops of three major coating sectors in the Yangtze River Delta of China, namely cargo container coating, ship equipment coating, and furniture coating, to evaluate the non-cancer and cancer risk of toxic VOCs exposure to occupational painters under a normal working condition. The results show that the container coating had highest cancer risk (2.29 × 10⁻⁶–5.53 × 10⁻⁶) exceeding the safe limit of 1.0 × 10⁻⁶, while non-cancer risk of all workshops was lower than acceptable level of 1. Ethylbenzene and 1,2-dichloropropane should be targeted for priority removal during the container coating process in attempt to reduce adverse health effect on the occupational painters. This study helps better understand the health risk of VOCs exposure in coating workshops in China and provides information for policy-makers to formulate possible control of specific toxic compounds during coating process.

The severe and pervasive effects of multispecies foodborne microbial biofilms highlight the importance of rapid detection and diagnosis of contamination risk in the field using epifluorescence-based techniques (EBT) combined with automatic image-counting software. This study screened the hygiene quality of the environment, the carcass and the slaughtering equipment in the El-Kharga abattoir, New Valley Province, Egypt, to assess possible contamination during slaughter process. In addition, biofilm was assessed, and bacteria was enumerated by epifluorescence microscopy. Using both conventional and EBT, the highest bacterial counts were observed for the slaughtering equipment (6.6 and 5.2 cfu/cm2, respectively), followed by different parts of the carcass (4.1 and 4.4 cfu/cm2, respectively) and environmental samples (3.9 and 4.1 cfu/cm2, respectively). A high prevalence of E. coli O157:H7 was observed on the slaughtering equipment (25%), which also led to carcass (1%) contamination. Moreover, Enterobacteriaceae members were detected during examination, such as Klebsiella pneumoniae, Enterobacter aerogenes, and Raoultella ornithinolytica. Despite the relatively good hygiene quality of the abattoir environment, there is also a high risk associated with biofilm formation by pathogenic microorganisms on the slaughtering equipment. Moreover, EBT showed different structures of the biofilm, including those formed at different maturation stages, such as voids, microbubbles, channels and mushroom shapes. (EBT) microscopy combined with image-counting software could be a candidate substitute to estimate efficiently, precisely and rapidly the microbial aggregation and exposure risk in field than the conventional counting techniques.

Kitchen emissions are mixed indoor air pollutants with adverse health effects, but the large-scale assessment is limited by costly equipment and survey methods. This study aimed to discuss the application of backpropagation (BP) neural network models in the assessment of kitchen emissions based on the exposure marker. A total of 3686 participants were recruited for the kitchen survey, and their sleep quality was measured by the Pittsburgh sleep quality index (PSQI). After excluding the confounders, 365 participants were selected to assess their urinary hydroxy polycyclic aromatic hydrocarbons (OH-PAHs) concentrations by ultra-high-performance liquid chromatography/tandem mass spectrometry. Two BP neural network models were then set up using the survey and detection data from the 365 participants and used to predict the total urinary OH-PAHs concentrations of all participants. The total urinary OH-PAHs and 1-hydroxy-naphthalene (1-OHNap) concentrations were significantly higher among the 365 participants with poor sleep quality (global PSQI score > 5; P < 0.05). Results from internal and external validation showed that our model has high credibility (model 2). Further, the participants with higher predicted total urinary OH-PAHs concentrations were associated with the global PSQI score of >5 (odds ratio (OR) = 1.284, 95% confidence interval (CI) = 1.082–1.525 for participants with predicted total urinary OH-PAHs concentrations of over 1.897 μg/mmol creatinine in model 1, and OR = 1.467, 95% CI = 1.240–1.735 for participants with predicted total urinary OH-PAHs concentrations of over 2.253 μg/mmol creatinine in model 2) after adjusting for the confounders. Findings suggest that the BP neural network model is suitable for assessing kitchen emissions, and the urinary OH-PAHs concentrations can be taken as the model outlay.