Street dust forms as a result of the interaction of the atmosphere, lithosphere (pedosphere) and anthroposphere and can be regarded as an index of the condition of the environment in urban areas. At the end of the twentieth century, there was a significant decrease in heavy metal emissions in Europe, but not so intensive in Poland. The question arises: Is the intensity of pollution still decreasing? The study objective was to assess changes in street dust pollution with heavy metals in Lublin (E Poland) in the years 2013 and 2018. The sample collection sites (68) were located within streets with a varying intensity of motor traffic. Cd, Cr, Cu, Ni, Pb and Zn concentrations were determined in two dust fractions, 63–200 μm and < 63 μm, by means of an X-ray fluorescence spectrometer. The levels of street dust pollution with heavy metals, expressed both in absolute concentrations and geochemical indices, were lower in 2018 than those in 2013. The clearest decrease of concentration levels occurred within the main roads, in the 63–200 μm fraction for Cu and Cd, and in both fractions for Pb. The mean concentrations of the investigated metals, normalised to the background values, are in the following order for both fractions in 2013 and 2018: Zn > Cd > Cu > Cr > Pb > Ni. Metals form the following order for Igₑₒ and EF: Zn > Cd > Cu > Pb > Cr > Ni. This order is slightly different for the ecological risk factor: Cd > Cu > Pb > Zn > Cr > Ni. In general, street dust in Lublin does not show contamination with Cr, Ni and Pb. Igₑₒ and EF indices show moderate levels of pollution with Cu, Cd and Zn.

In the present study, a simple, fast, and economical method was introduced to eliminate methylene blue from dye wastewater water using a non-toxic, inexpensive, stable, and efficient adsorbent. The poly (vinyl alcohol) (PVA)/starch hydrogel nanofiber membrane with high surface area and the three-dimensional structure was fabricated in water via electrospinning strategy, and the cross-linking reaction was done by thermal treatment. The characterization of the nanofibers was carried out using Fourier-transform infrared spectrometer (FT-IR) and field-emission scanning electron microscopy (FE-SEM), and the cross-linked PVA/starch nanofiber was applied as a membrane for the removal of methylene blue (MB). The recovery of MB was performed by methanol solution containing 5% (v/v) HCl. Langmuir isotherm model successfully described the adsorption of MB on nanosorbent, and the maximum adsorption capacity (qₘ) was 400 mg g⁻¹. Also, the kinetic of adsorption was well fitted by the pseudo-second-order model. In this study, because of the high stability of fabricated membrane (based on the tensile testing), it can be used as a filter for the fast separation of MB (cationic dye) and methyl orange (MO, anionic dye). Graphical abstract

Dispersants including Tween 20, Tween 40, Tween 60, and polyacrylic acid (PAA) were used to modify nanoscale zero-valent iron (nZVI). All dispersants dispersed nZVI effectively. PAA-modified nZVI was more stable than nZVI that was modified with Tween surfactant. Iron nanoparticles that were prepared using 0.5–5.0% (vol%) of PAA remained in suspension for more than 2 h. nZVI that was modified using Tween surfactant remained in suspension for 30–60 min, and there was complete sedimentation of bare iron in 10 min. When 2.0–5.0% (vol%) of Tween surfactant was used, the stability of the nZVI that was modified using Tween 20 was much better than that for nZVI that was modified using Tween 40 or Tween 60. The results for the transportation test show that nZVI that was prepared using 2% (vol%) of Tween 20 exhibited the best mobility in porous media. Approximately 83–90% of TCE was degraded by bare, PAA-modified, and Tween 20-modified nZVI, and about 63–67% of TCE was removed by nZVI that was modified using Tween 40 and Tween 60 during 20 days of reaction. The production of cis-dichloroethene (DCE) and 1,1-DCE demonstrates that TCE is removed via reductive dechlorination. The results of this study show that PAA- and Tween 20-modified nZVI are more practical for in situ remediation because they exhibit good mobility and degrade TCE effectively.

Leachate is one of the main surface water pollution sources in Selangor State (SS), Malaysia. The prediction of leachate amounts is elementary in sustainable waste management and leachate treatment processes, before discharging to surrounding environment. In developing countries, the accurate evaluation of leachate generation rates has often considered a challenge due to the lack of reliable data and high measurement costs. Leachate generation is related to several factors, including meteorological data, waste generation rates, and landfill design conditions. The high variations in these factors lead to complicating leachate modeling processes. This study aims at identifying the key elements contributing to leachate production and developing various AI-based models to predict leachate generation rates. These models included Artificial Neural Network (ANN)-Multi-linear perceptron (MLP) with single and double hidden layers, and support vector machine (SVM) regression time series algorithms. Various performance measures were applied to evaluate the developed model’s accuracy. In this study, input optimization process showed that three inputs were acceptable for modeling the leachate generation rates, namely dumped waste quantity, rainfall level, and emanated gases. The initial performance analysis showed that ANN-MLP2 model—which applies two hidden layers—achieved the best performance, then followed by ANN-MLP1 model—which applies one hidden layer and three inputs—while SVM model gave the lowest performance. Ranges and frequency of relative error (RE%) also demonstrate that ANN-MLP models outperformed SVM models. Furthermore, low and peak flow criterion (LFC and PFC) assessment of leachate inflow values in ANN-MLP model with two hidden layers made more accurate values than other models. Since minimizing data collection and processing efforts as well as minimizing modeling complexity are critical in the hydrological modeling process, the applied input optimization process and the developed models in this study were able to provide a good performance in the modeling of leachate generation efficiently.

Biodiesel production from microalgae has been researched extensively and attempted to commercialize on a large scale, but there are major hurdles in the production process like harvesting and low lipid content, which should be studied to enhance the process and make it economical. Present study aimed to improve the lipid productivity of Chlorella minutissima and Chlorella pyrenoidosa by modifying the carbon and nitrogen content of the medium. Both organisms were grown in BG11 medium for the first 6 days and thereafter grown in a modified BG11 medium completely deprived of nitrogen for 2 to 10 days. Nitrogen deprivation increased the lipid productivity of Chlorella minutissima to 20% and that of Chlorella pyrenoidosa to 17.6% by day 6. This was further coupled with carbon addition in the form of citric acid (5 g/L), sodium acetate (5 g/L), sodium carbonate (5 g/L), and sodium potassium tartarate (5 g/L), which increased the total lipid productivity of Chlorella minutissima up to 24% and that of Chlorella pyrenoidosa up to 23%. The highest lipid productivity of up to 24% for Chlorella minutissima and up to 23% for Chlorella pyrenoidosa was observed with nitrogen deprivation coupled with sodium acetate. Acidic transesterification revealed the presence of fatty acid methyl esters, majority of which consisted of hexadecanoic acid methyl ester and octadecanoic acid methyl ester. Maximum of 3% fatty acid methyl esters for Chlorella minutissima and 4% for Chlorella pyrenoidosa were obtained under nitrogen deprivation and sodium acetate as a carbon source. Thus, nitrogen deprivation coupled with sodium acetate as an increased carbon source in BG11 medium helps to increase the lipid productivity of Chlorella minutissima and Chlorella pyrenoidosa, and produces long-chain fatty acid methyl esters of C17 and C19 along with C21, C25, and C29.

Vegetation is a natural tie that connects the atmosphere, hydrosphere, biosphere, and pedosphere. Quantitatively evaluating the variability of vegetation coverage and exploring its associated influence factors are essential for ecological security and sustainable economic development. In this paper, the spatiotemporal variation of vegetation coverage and its response to climatic factors and land use change were investigated in the Yangtze River Delta (YRD) from 2001 to 2015, based on normalized difference vegetation index (NDVI) data, vegetation type data, climate data, and land use/cover change (LUCC) data. The results indicated that the annual mean vegetation coverage revealed a nonsignificant decreasing trend over the whole YRD. Areas characterized by significant decreasing (P < 0.05) trends were mainly concentrated on the central and northern part of the YRD, and significant increasing (P < 0.05) trends were mainly located in the southern part of the study area. Except for grassland and cultivated crops, vegetation coverage of the other types of vegetation was all exhibiting increasing trends. Temperature has a more pronounced impact on vegetation growth than precipitation at both the annual and monthly scales. Furthermore, vegetation growth exhibited a time lag effect for 1~2 months in response to precipitation, while there was no such phenomenon with temperature. Land use change caused by urbanization is an important driving factor for the decrease of vegetation coverage in the YRD, and the effect of land use change on the vegetation dynamic should not be overlook.

Over the past years, there were dramatic improvements in identifying and assessing various feedstocks for the production of biodiesel fuels. To promote a particular feedstock as a renewable source of energy, it is important to analyze their energy, economic, and engine performance characteristics. The current work attempts to evaluate the net energy and economic indices for both fossil diesel and coconut-blended diesel (B20) considering the diesel consumption by the Indian railways. Further, we present the experimental results of a multi-cylinder diesel engine operated with neat coconut biodiesel (B100) and fossil diesel at various load and speed conditions. The engine experiments reveal that the coconut biodiesel exhibits leaner combustion and shorter ignition delay than fossil diesel. Lower amount of carbon monoxide, hydrocarbon, and smoke emission is observed in the case of coconut biodiesel, with higher levels of nitric oxide (14%) and fuel consumption than diesel. The coefficient of variation in indicated mean effective pressure is within the range of better driveability zone for both the fuels at all test conditions. Overall the engine performance, emission and combustion results with neat coconut biodiesel are favorable with a penalty in NO emission at high load conditions. The techno-economical study highlights higher production cost per liter of B20 than the cost of fossil diesel. However, the net energy ratio (NER) for B20 is 1.021, favoring higher output than diesel and thus lowers the dependency on crude oil.

There are mounting evidences indicated that maternal exposure to outdoor air pollutants in pregnancy affects children’s neural development, but the researches on children’s behavioral difficulties are seldom. We explored the association between maternal exposure to outdoor air pollution during different trimesters of pregnancy and the prevalence of behavioral difficulties among 657 preschool children aged 3–4 from three kindergartens in Wuhan, China. This is a cross-sectional study. Children’s behavioral difficulties were assessed by the Strengths and Difficulties Questionnaire (SDQ) (reported by parents). Maternal exposure to outdoor air pollutants during pregnancy were estimated based on the daily average measured concentration levels from ground monitoring stations. Potential confounding factors including children-related, maternal, and socio-economic status (SES) were adjusted in the study. We calculated the prevalence of each type of behavioral difficulties and used binary logistic regression method to estimate the crude odds ratio (cOR), adjusted odds ratio (aOR), and corresponding 95% confidence intervals (95% CIs) for 1 μg/m³ increase in each air pollutant during every exposure window in single- and two-pollutant models. The prevalence of participants’ total behavioral difficulties was 9.6%. In single-pollutant models, during full gestation, positive associations were observed between exposure to NO₂ (aOR = 1.204, 95% CI 1.042, 1.392), particle matter (PM)₁₀ (aOR = 1.070, 95% CI 1.018, 1.125), PM₂.₅ (aOR = 1.095, 95% CI 1.021, 1.176) and total difficulties, exposure to PM₁₀ (aOR = 1.040, 95% CI 1.001, 1.081), PM₂.₅ (aOR = 1.053, 95% CI 1.000, 1.109) and prosocial behavior, respectively. In the first trimester, exposure to SO₂ (aOR = 1.047, 95% CI 1.009, 1.086), NO₂ (aOR = 1.039, 95% CI 1.013, 1.066), PM₁₀ (aOR = 1.013, 95% CI 1.004, 1.023), and PM₂.₅ (aOR = 1.016, 95% CI 1.004, 1.028) were all positively associated with total difficulties. The associations between second and third trimesters’ exposure to all pollutants and outcomes were not statistically significant. However, in the two-pollutant models, second trimester exposure to PM₂.₅ (aOR = 1.078, 95%CI 1.023, 1.137) was positively associated with total behavioral difficulties after adjusting for PM₁₀. Exposure to outdoor air pollutants SO₂, NO₂, PM₁₀, and PM₂.₅ during pregnancy may be associated with behavioral difficulties, especially in the first trimester.

The occurrence of persistent organic pollutants (POPs) and polycyclic aromatic hydrocarbons (PAHs) in the atmosphere of six sites with different emission sources in the province of Córdoba, Argentina, was analyzed. The sites included urban, industrial, agricultural, and mountain areas. Samples were collected using passive air samplers (PAS) consisting of polyurethane foam disks (PUF). Samples were analyzed for 12 PAHs, 31 polychlorinated biphenyls (PCBs), 12 organochlorine pesticides (OCPs), and 11 polybrominated diphenyl ethers (PBDEs). The concentrations of PAHs in the atmosphere were elevated at urban sites and were even higher at the industrial site. With respect to OCPs, it was observed that the concentrations of endosulfan were greater at the agricultural site (AGR) (416 ± 4 pg m⁻³). For hexachlorocyclohexanes (HCHs), only the alpha isomer was detected and there were minimal differences between the different sampling sites (5.9–13.3 pg m⁻³). In the case of dieldrin, the highest concentrations (33.6 pg m⁻³) were found at the mountain site, which may have been due to its use for insect control. Although heptachlor epoxide was not detected, the concentration of heptachlor was significantly higher at the agricultural and downtown sites (∼ 3.6 pg m⁻³). Regarding DDTs, the isomers p,p′-DDT and p,p′-DDE showed the highest concentrations at the mountain site (ΣDDT 120 ± 12 pg m⁻³) and downtown site (ΣDDT 157 ± 62 pg m⁻³). The relationship between the isomers suggested that at the downtown site, the contribution of this pesticide to the environment was recent, probably for the control of diseases vectors. The congener pattern of PBDEs was dominated by BDE-47, and BDE-99 at all sites, with the downtown site having the highest concentrations of compound esters (ΣPBDEs 118 ± 38 pg m⁻³). Finally, high concentrations of PCBs were found at the industrial site (ΣPCBs 1677 ± 134 pg m⁻³), and the predominating homologs were 5-Cl and 6-Cl, in contrast to the other sites where PCBs were dominated by 3-Cl and 4-Cl. This is the first study of POPs carried out in the province of Córdoba.