Very few publications have compared different study designs investigating the short-term effects of air pollutants on healthcare visits and hospitalizations for respiratory tract diseases. This study describes, using two different study designs (a case-crossover design and a time-series analysis), the association of air pollutants and respiratory disease hospitalizations. The study has been conducted on 5 cities in Poland on a timeline of almost 4 years. DLNM and regression models were both used for the assessment of the short-term effects of air pollution peaks on respiratory hospitalizations. Both case-crossover and time-series studies equally revealed a positive association between air pollution peaks and hospitalization occurrences. Results were provided in the form of percentage increase of a respiratory visit/hospitalization, for each 10-μg/m³ increment in single pollutant level for both study designs. The most significant estimated % increases of hospitalizations linked to increase of 10 μg/m³ of pollutant have been recorded in general with particulate matter, with highest values for 24 h PM₂.₅ in Warsaw (6.4%, case-crossover; 4.5%, time series, respectively) and in Białystok (5.6%, case-crossover; 4.5%, time series, respectively). The case-crossover analysis results have shown a larger CI in comparison to the results of the time-series analysis, while the lag days were easier to identify with the case-crossover design. The trends and the overlap of the results occurring from both methods are good and show applicability of both study designs to air pollution effects on short-term hospitalizations.

The effects of anthropogenic noise have garnered significant attention in marine ecosystems, but comparatively less is known about its impacts on freshwater ecosystems. For fish that provide parental care, the effects of acoustic disturbance could have fitness-level consequences if nest tending behavior is altered. This study explored the effects of motorboat noise on the parental behavior of nesting male smallmouth bass (Micropterus dolomieu; Lacépède, 1802), an important freshwater game fish in North America that provides sole paternal care to offspring. Specifically, we evaluated how boat noise proximity to a bass nest (ranging from 4.5 to 90 m) influenced paternal care behaviors. A total of 73 fish were exposed to a 3-min motorboat playback designed to simulate a boat sound that typically occurs in areas near littoral nesting sites. The fish were video recorded, and their behaviors were analyzed before, during, and after exposure to the playback. Residency time was the only behavioral metric to be adversely affected by noise playbacks but only when in close proximity to the speaker. Our results suggest that boat noise may have an impact on bass reproductive fitness in specific contexts where combustion motors are used near shore during the nesting period. The largely null findings may indicate a resilience to boat noise and/or habituation to the noise. In addition, boats displace water and create waves that represent another form of disturbance that could be experienced by fish but was not simulated here. Future research should integrate behavioral and physiological responses to boat noise and other aspects of boat disturbance to better understand the fitness impacts of boating activity on freshwater fish.

In this study, we evaluated the feasibility of treating water contaminated with ketoprofen (KET) by ultrasound (US) and sono-activated persulfate (US/PS) systems. The effects of different reaction parameters, such as the initial pharmaceutical compound concentration (C₀), persulfate concentration, ultrasonic power, and initial pH of the solution (pHᵢ) on the KET removal and reaction kinetics were investigated. Tert-butyl alcohol (TBA) was used as a chemical probe to identify the predominant radicals in the US and US/PS systems. The results demonstrated that the persulfate oxidant can be activated by US under neutral and alkaline conditions. Moreover, KET was predominantly degraded by hydroxyl radicals (HO.) generated by the activation of persulfate. Under alkaline conditions, the removal efficiency of KET improved with increasing amount of persulfate added. HO. was the dominant radical at a more alkaline pH in both US and US/PS systems, which was verified by the tert-butyl alcohol probe. These results provide insights into the treatment of water contaminated with pharmaceutical compounds.

This paper describes the removal of Cr(VI) using the combination of bacterial reduction of Cr(VI) to Cr(III) by wood husk–packed column containing the Cr(VI)-reducing biofilm followed by Cr(III) removal using the coagulation-flocculation technique. The chromium removal process was carried out at the laboratory-scale for 90 days using different batches of Cr(VI) ranging from 35 to 231 mg L⁻¹. Analysis of variance (ANOVA) showed a high coefficient of determination (R²) value of 0.9941, thus ensuring a satisfactory adjustment of the second-order regression model with the experimental data. The experimental observations were in reasonable agreement with the modeled values. The biofilm was able to completely reduce 100 mg L⁻¹ Cr(VI) in 6 h while a longer contact time (18 h) was needed for higher Cr(VI) concentrations. In this study, ORP (oxidation-reduction potential) was used as the control parameter during the Cr(VI) reduction process. The coagulation/flocculation process using the combination of alum and polyacrylamide results in complete removal of color, 85% Cr(III), and 97% turbidity. The field emission scanning electron microscope (FESEM) analysis of the biofilm revealed the embedding of bacterial cells in extracellular polymeric substances (EPS). This study successfully demonstrated the potential application of a bacterial biofilm and chemical systems to remove chromium contamination from water systems.

Harmattan dust and traffic-related pollution have been a serious environmental concern in the West African sub-region. In order to further contribute to the understanding of ambient levels of atmospheric pollution and chemical composition in the region, this study monitored harmattan dust and traffic-related particulate matter at four locations across southwestern (Ile-Ife) and north-western (Zaria) geo-political zones of Nigeria. The collected samples were characterized for their chemical composition using Energy-Dispersive X-Ray Fluorescence spectrometer equipped with an optimized secondary target x-ray excitation conditions (Al, CaF₂, Fe, Ge, Zr, Mo, Ag, Al₂O₃). The objectives are to assess spatio-temporal mass concentrations, chemical footprints, enrichment factors, elemental correlations, and ratios at all locations. The X-ray analytical method was validated with a NIST SRM 2783 air particulate standard, and detection limits for each chemical specie were determined. Validation results showed good reproducibility of the certified reference material with relative standard deviations of the elements much lower by about 1–13% than the corresponding reference values. Mass concentrations reached up to 2200 μgm⁻³ in the north and 1500 μgm⁻³ in the south. The range of mean concentration of crustal marker elements were Al (5–27 μgm⁻³), Si (5–856 μgm⁻³), Ca (0.78–13 μgm⁻³), and Fe (2–13 μgm⁻³), and were most abundant during the harmattan particularly in the southwestern region. Highest mean concentration values of 380, 810, and 420 ngm⁻³ were recorded for Cr, Cu, and Pb respectively at the traffic corridor which also recorded the highest enrichment factors. Black carbon and elemental concentrations contributed between 1 to 54% and 9 to 94% across the locations respectively. Backward trajectories of atmospheric flow over the locations showed two dominant sources; dust laden source from the Sahara desert and maritime flow over the Gulf of Guinea. This study found that chemical footprints (Al, Si, K, Ca, Ti, and Fe) of harmattan-related dust were more correlated (r² between 0.88 and 0.99) than those attributed to dust re-suspension at the traffic location.

In the absence of adequate molecular oxygen in a continuously flooded soil, other oxidizing anions can potentially oxidize arsenite (As(III)) into arsenate (As(V)) and reduce the bioavailability of arsenic (As) to rice while maintaining high rice yield. We conducted a greenhouse study to evaluate the effect of two prevalent oxyanions (10 mg/L nitrate and/or 50 μg/L perchlorate) on the As uptake, speciation, and accumulation in a hybrid rice (XL753) at the heading and maturity stages. The presence of nitrate and/or perchlorate at the used concentrations increased the rice grain yield by 35–93% to16.6–23.8 g/pot while lowering the total As in rice grains by 34–45% to 0.81–0.97 mg/kg dry weight. Perchlorate alone led to the greatest decrease in total As. Organic As was the predominant species in rice grains, with dimethylarsinic acid (DMA) accounting for 66–76% of total As in all treatments. In contrast, inorganic As was the dominant As form in rice straws and roots, with As(V) accounting for 62.4–91.4% of total As in all treatments. The translocation and accumulation of different As species in rice tissues varied at different growth stages in the presence of two tested oxyanions, as indicated by the ratios of inorganic vs organic As and inorganic As(III) vs As(V). Overall, the presence of oxyanions in irrigation water at the tested concentrations significantly decreased the total As accumulation in rice grains, while enhancing the rice yield.

Limited epidemiologic studies questioned the association between pre- and postnatal lead exposure and the development of cerebral palsy (CP). Moreover, the genotypes of δ-aminolevulinic acid dehydratase (δ-ALAD) in CP patients and their mothers and their association to the blood lead levels (BLLs) were not previously studied. This study aimed to evaluate the association between δ-ALAD gene polymorphism and BLL in cases of CP and their mothers. A case control study was carried out on 23 CP cases and equal number of healthy matched controls. The mothers of the included children were asked to answer a questionnaire involving the baseline clinical and demographic characteristics. Also, questionnaires were done to detect the sources of environmental lead exposure and screen lead exposure during the pregnancy period. BLL, δ-ALAD enzyme activity, and genetic analysis for ALAD G177C were done for each child and his mother. There was significant (p < 0.001) elevation of BLL in CP cases and their mothers that was positively correlated (r = 0.436, p < 0.05). There were progressive decreases in δ-ALAD activity with increasing BLL in both children and mothers (p < 0.05). There were non-significant (p > 0.05) differences between CP and the control group regarding frequency of ALAD G177C genotypes, while there was a significant (p = 0.04) increase in the frequency of ALAD 1-2 (GC) genotype in the mothers of the CP group associated with high BLL and significant decrease in δ-ALAD activity (p < 0.001). The study can indicate the significance of δ-ALAD gene polymorphism in the prenatal exposure to lead and the affection of the developing brain, pointing to the importance of controlling lead in pregnant women especially those with ALAD 1-2 genotype.

Accurate prediction of salinity concentration in the aquifer in response to fluctuating groundwater pumping pattern is an essential component of any coastal groundwater planning and management framework. Data-driven prediction models have been proved efficient in predicting groundwater salinity levels in coastal aquifers. The use of ensemble prediction models is known to be more accurate with robust prediction capabilities when compared with standalone prediction models. This study compares the performances of homogeneous and heterogeneous ensemble models for groundwater salinity predictions. A homogeneous ensemble model is composed of several standalone models of the same type (i.e. employs one machine learning tool) whereas a heterogeneous ensemble model is composed of several standalone models of different types (i.e. employs multiple machine learning tools). Specifically, homogeneous and heterogeneous ensemble models of various standalone machine learning tools such as artificial neural network (ANN), genetic programming (GP), support vector regression (SVR), and Gaussian process regression (GPR) are developed to predict groundwater salinity concentrations in a small Pacific island coastal aquifer system. Standalone and ensemble prediction models are trained and validated using identical pumping and resulting salinity concentration datasets obtained by solving numerical 3D transient density-dependent coastal aquifer flow and transport model. After validation, the ensemble models are used to predict salinity concentration at selected monitoring wells in the modelled aquifer under variable groundwater pumping conditions. Prediction capabilities of the developed ensemble models are quantified using standard statistical procedures. The performance evaluation result suggested that the predictive capabilities of the developed standalone prediction models (ANN, GP, SVR, and GPR) were comparable with the numerical groundwater variable density-dependent flow and salt transport model. However, GPR standalone models had better prediction capabilities when compared with the other standalone models. Also, SVR and GPR standalone models were more efficient (i.e. took less computational training time) than other standalone models. In terms of ensemble models, the performance of the homogeneous GPR ensemble model was established to be superior to other homogeneous and heterogeneous ensemble models. The homogeneous GPR ensemble model was favoured both in terms of efficiency. Overall, based on the limited performance evaluation result, GPR homogeneous model was considered to be the best prediction model when compared with all the standalone models, other homogeneous ensemble model, and the heterogeneous ensemble model. Therefore, it can be utilised as a reliable groundwater salinity prediction tool and also used as an approximate simulator in coupled simulation-optimization models needed for prescribing optimal groundwater management strategies.

Elevated salinity creates degrading conditions for the development of aquatic biota in different regions of the world. There is a need for research on freshwater salinisation in order to understand how this stressor alters ecosystem function and to predict changes in biodiversity globally. Such data are missing from Central Europe, and therefore, the presented study was performed in inland anthropogenic ponds with different salinity levels located in the second largest European hard coal basin. The researcher indicated a positive correlation between water salinity and the biomass and density of macrozoobenthos as well as the percentage of shredders and the abundance of alien species, whereas there was a decrease in taxa diversity and richness and the abundance of filtering and gathering collectors and predators along with increasing salinity. The survey showed that a high level of nutrients and organic matter were also significantly correlated with the distribution of the macroinvertebrate taxa and functional feeding groups. The conducted research confirmed that mining salinisation acts as a strong filter that shapes the biodiversity because it affects the composition, abundance, biomass and functional traits of benthic macroinvertebrates and significantly contributes to the invasion of alien species.

Over the previous two decades, Chinese economic development presented a rapid growth. However, with continuous industrialization and urbanization, China is confronted with great challenges of energy security and environmental issues. These problems are closely related to the current accounting method of economic growth to a certain extent. In order to meet these challenges, it is imperative to establish a green accounting system of economic growth and measure China’s green GDP and its changing trend based on the industrial perspective. Using the System of Environmental Economic Accounting (SEEA) and industry data, this paper estimates China’s green GDP and green value added by industry sectors in 2005, 2007, 2010, 2012, 2015, and 2017. The results reveal the following: First, the ratio of green GDP to traditional GDP gradually increases from 89.85 to 95.83% during 2005–2017, which means that the negative externalities of economic growth of the resource and environment are gradually weakened. Second, the difference between traditional GDP and green GDP during 2005–2017 is about 6.96%, with the carbon emissions accounting for 70.71% of environmental impact. Third, due to more than 80% of the environmental impact coming from three sectors: manufacturing (49.99%), electricity industry (22.63%), and other services (11.37%), these three sectors should be key sectors for energy conservation and emission reduction; fourth, the green GDP of the mining, electricity industries, and manufacturing accounts for the lowest proportion of GDP, which means that the development patterns of these three industries in recent years should be adjusted and optimized step by step.