Transboundary pollution control usually requires the cooperation of neighboring countries due to the externality of pollution. However, countries at different levels of development, which are called asymmetric countries in this paper, may have different attitudes toward this cooperation. The developing countries would like to take a free ride and they can benefit from the cooperation with developed countries, but the developed countries may not be willing to afford this cooperation cost. This paper discusses the cooperation between two asymmetric countries that developed country may provide assistant investments to help the developing country reduce pollution stock. We consider a dynamic differential game to model the transboundary pollution control between two asymmetric regions and derive the optimal equilibrium of both regions using the Hamilton–Jacobi–Bellman (HJB) equation. To explore the impact of assistant investments, numerical illustrations and sensitivity analysis are implemented to compare the equilibrium strategies under two scenarios: that with or without assistant investments. We conclude that the common pollution stock will be reduced when the developed country is willing to provide assistant investments to the developing country. Besides, the equilibrium emission strategies of both countries increase with assistant investments, which means more economic benefits for both sides.

Qanats in the aquifer of the Tehuacán Valley (Mexico) represent an ancient way of using groundwater that is still practiced today. They are used mainly for agricultural irrigation. However, anthropogenic activities have jeopardized the use of these aquifers. We analyzed 24 qanats in the Tehuacán Valley to assess water quality. Based on 24 physicochemical variables, a water quality index (WQI) was constructed on a zero-to-100 scale, divided into five water quality classes. A decision-tree analysis was applied to identify the parameters with the highest influence on the WQI, considering the water quality classes as categorical responses and the values of physicochemical variables as drivers of these categories. We produced interpolation maps to identify trends. The relationship between the WQI and the normalized difference indices of vegetation and salinity (NDVI and NDSI, respectively) was analyzed using a ternary diagram. WQI scores showed that 12.5% of the qanats have very good quality; 25%, good quality; and the remaining (62.5%) range from moderate to unacceptable quality. The CHAID classification-tree method correctly explained 83.3% of the categories, with sulfates, alkalinity, conductivity, and nitrates as the main parameters that explain water quality. WQI was inversely related to NDVI and NDSI, showing seasonal differences. Interpolation maps suggest a better water quality in the northern zone of the aquifer.

To control the dust pollution caused by open-pit coal mining and reduce or avoid the secondary hazards of existing dust suppressants to the environment, a biological dust suppressant was prepared through the synergistic effect of a surfactant and an enzyme-induced carbonate precipitation. The optimal ratio of biological dust suppressant was determined, and the dust suppressive effect and dust consolidation mechanism of the biological dust suppressant were investigated. The results showed that the optimal biological dust suppressant had an alkyl polyglycoside (APG) concentration of 0.3 wt.%, a urea-CaCl₂ concentration of 0.6 mol/L, and a urease to urea-CaCl₂ volume ratio of 1:3. The wind erosion resistance of coal dust treated with this dust suppressant was enhanced by 86.69%. The adsorption of the biological dust suppressant by coal dust was mainly due to the electrostatic interaction between the surfactant and coal dust. The mineralization product of the dust suppressant was calcite-type CaCO₃, which consolidated coal dust due to the formation of intermolecular hydrogen bonds between CaCO₃ and coal dust.

Mercury (Hg) is a great concern for marine environments. Bird feathers have been widely used to assess Hg pollution. In this study, we determine mercury concentrations in body feathers of juvenile Magellanic penguins (Spheniscus magellanicus) from the southeastern sector of Buenos Aires province, Argentina, during a non-breeding season, considering both sexes. Hg levels, considering both females and males together, ranged between 265.5 and 1515.52 ng/g. These levels are well below the concentrations in feathers suggested for taking actions focused on the protection of seabirds. Non-significant differences between sexes regarding Hg levels were found, probably because juveniles were sexually immature and females did not excrete Hg by egg laying yet. Hg concentrations found in this study were an order of magnitude higher than those reported 10 years ago for the species in breeding areas on the Argentine coast. Thus, the present study provides relevant information indicating a possible increase of Hg pollution in the southwestern region of the Atlantic Ocean and thus trigger for the development of monitoring programs and regional strategies to improve the conservation status of this species.

Increasing petroleum consumption and a rise in incidental oil spillages have become global concerns owing to their aquatic and terrestrial toxicity. Various physicochemical and biological treatment strategies have been studied to tackle them and their impact on environment. One of such approaches in this regard is the use of microbial processes due to their being “green” and also apparent low cost and high effectiveness. This review presents the advancement in the physical and biological remediation methods and their progressive efficacy if employed in combination of hybrid modes. The use of biosurfactants and/or biochar along with microbes seems to be a more effective bioremediation approach as compared to their individual effects. The lacuna in research at community or molecular level has been overcome by the recent introduction of “-omics” technology in hydrocarbon degradation. Thus, the review further focuses on presenting the state-of-art information on the advancement of petroleum bioremediation strategies and identifies the research gaps for achieving total mitigation of petroleum oil.

COVID-19, declared by the World Health Organization (WHO) to be a pandemic, has affected greenhouse gas emissions and contributed to the uncertainty of environmental activities. This study demonstrates the effect of lockdowns, the number of new confirmed cases, and the number of newly confirmed deaths due to COVID-19 on CO₂ emissions. The data series used are for the UK from 23 March 2020 to 31 December 2020 and for Spain from 14 March 2020 to 31 December 2020. This research adopted the Augmented Dickey–Fuller (ADF) test for a stationarity check of the data series, the Johansen cointegration test for determining cointegration among variables, and the vector error correction model (VEC) Granger causality test for directional cause and effect between exogenous and endogenous variables. The VEC model shows a bidirectional relationship between CO₂ emissions and lockdown and a unidirectional relationship with newly confirmed cases and deaths for the UK. The results of Spain confirmed the unidirectional relationship of CO₂ emissions, lockdown, new confirmed cases, and deaths. The Granger causality test reconfirms the relationship of variables except for newly confirmed deaths for the UK and newly confirmed cases for Spain. Conclusively, the pandemic breakout reduced the emission of CO₂. The directional relation of variables supported the short-run relationship of CO₂ emissions with newly confirmed cases and deaths, while a long- and short-run relationship was shown with lockdown. The directional and relational behavior of lockdown potentially linked the CO₂ emissions with daily life activities.

The treatment of municipal wastewater produces clean water and sewage sludge (MSS), the management of which has become a serious problem in Europe. The typical destination of MSS is to spread it on land, but the presence of heavy metals and pollutants raises environmental and health concerns. Bioconversion mediated by larvae of black soldier fly (BSFL) Hermetia illucens (Diptera, Stratiomyidae: Hermetiinae) may be a strategy for managing MSS. The process adds value by generating larvae which contain proteins and lipids that are suitable for feed and/or for industrial or energy applications, and a residue as soil conditioner. MSS from the treatment plant of Ladispoli (Rome province) was mixed with an artificial fly diet at 50% and 75% (fresh weight basis) to feed BSFL. Larval performance, substrate reduction, and the concentrations of 12 metals in the initial and residual substrates and in larval bodies at the end of the experiments were assessed. Larval survival (> 96%) was not affected. Larval weight, larval development, larval protein and lipid content, and waste reduction increased in proportion the increase of the co-substrate (fly diet). The concentration of most of the 12 elements in the residue was reduced and, in the cases of Cu and Zn, the quantities dropped under the Italian national maximum permissible content for fertilizers. The content of metals in mature larvae did not exceed the maximum allowed concentration in raw material for feed for the European Directive. This study contributes to highlight the potential of BSF for MSS recovery and its valorization. The proportion of fly diet in the mixture influenced the process, and the one with the highest co-substrate percentage performed best. Future research using other wastes or by-products as co-substrate of MSS should be explored to determine their suitability.

Raw hides/skins are considered to be the prime component for leather industry, which once flayed from animals, plummets to microbial attack. Their preservation combats putrefaction wherein curing using sodium chloride (NaCl) is by and large the most widely accepted method. However, there are few stumble blocks in using NaCl in terms of pollution load generated such as high total dissolved solids (TDS), total suspended solids (TSS), biological oxygen demand (BOD), chemical oxygen demand (COD) and chlorides (Cl⁻). Additionally, this effluent when discharged affects the quality of the water, soil and plants causing huge ecological damage. To evade these problems, researches are being carried out to explore alternative preservation techniques which are either salt free or with reduced amount of salt. Different methods were proposed time and again which remained unfeasible due to associated drawbacks like high cost, health hazards and environmental concerns. Therefore, finding cheaper, eco-friendly and sustainable method for preservation has become the need of the hour for this industry. This review meticulously summarizes the changing trends in preservation techniques for past few decades with special emphasis on bio-based preservation. The diversity of the natural preservatives explored for the said purpose has been systematically reviewed. The enormous environmental benefits that can be obtained by adopting bio-based preservation and future avenues of research have been discussed.

Sapindus mukorossi (S. mukorossi) is an important biological washing material and biomass energy tree species whose peel is rich in saponins, and its kernels have a high oil content. We used the maximum entropy model (MaxEnt) to predict the suitable habitats of S. mukorossi globally, screen the dominant environmental factors affecting its distribution and analyse the changes in its suitable habitats under climate change from prehistory to the future, and the results will provide a scientific basis for germplasm resource collection, protection, introduction and cultivation. Twenty-two environmental variables and global distribution data for S. mukorossi were used to construct the species distribution model, and the receiver operating characteristic (ROC) curve was used to verify the accuracy of the model. The dominant environmental factors were screened through the jackknife method, and then, the geographical information system (ArcGIS) was used to complete the grade of suitable habitat division and area calculation. The results showed that the MaxEnt model had an excellent predictive effect, and the area under the ROC curve (AUC) value was as high as 0.969. The precipitation of the warmest quarter (Bio18), the minimum temperature of the coldest month (Bio6), temperature seasonality (Bio4) and isothermality (Bio3) were the dominant environmental factors that affected the distribution of S. mukorossi. The largest area of the world’s suitable habitats occurred during the last interglacial (LIG) (772.69 × 10⁴ km²), and the area decreased sharply (614.46 × 10⁴ km²) during the last glacial maximum (LGM). The area of suitable habitat showed an increasing trend during the Mid-Holocene (MH) and currently, with areas of 631.06 × 10⁴ km² and 706.82 × 10⁴ km², respectively. The area of suitable habitats for S. mukorossi globally was 718.35 × 10⁴ km² (SSP1-2.6), 636.85 × 10⁴ km² (SSP2-4.5), 657.64 × 10⁴ km² (SSP3-7.0) and 675.89 × 10⁴ km² (SSP5-8.5) under the four scenarios of the future climate. The area increased only in the SSP1 scenario. In summary, globally, the suitable area of S. mukorossi tended to migrate to higher latitudes and decrease in area with future climate change.

The purpose of this research is to seek a better inversion algorithm. And on this basis, it explores the feasibility of using hyperspectral monitoring technology instead of laboratory physical and chemical index test and evaluates the prediction effect of inversion model on water quality change. So as to be more convenient, more economical and extensive monitoring methods for water quality monitoring of urban internal river are provided. This paper takes the water samples collected in Fuyang River in downtown Handan as the research object and obtains original spectral data of the samples by the ASD FieldSpec 4 field hyperspectral spectrometer. After the smoothing filter pretreatment by the Savitzky-Golay (SG) method and specified mathematical transformations, the modeling spectral indicators of various water quality parameters are selected and determined by calculating the maximum mean of absolute values for correlation coefficients of various spectral indicators and measured values in the wavelength range from 400 to 950 nm. By introducing partial least squares (PLS), random forest (RF), and Lasso (least absolute shrinkage and selection operator), six water quality parameter fitting models were constructed including turbidity (Turb), suspended substance (SS), chemical oxygen demand (COD), NH4-N, total nitrogen (TN), and total phosphorus (TP), which are also testified and evaluated through hyperspectral data. The results show that different spectral transformation methods highlight different information inversion effects. The first derivative of reciprocal logarithm of spectral data after SG smoothing has a good modeling effect on four water quality parameters including Turb, COD, NH₄-N, and TP; and the first derivative of smoothed spectral data has a good modeling effect on both water quality parameters of SS and TN. Among the three models, the PLS model has a good prediction effect, with the [Formula: see text] for COD, TN, and TP ranging from 0.74 to 0.80, while that for Turb and SS shows relatively poorer prediction effect, followed by even worse effect on HN₄-H. Both machine learning algorithms of RF and Lasso have respectively obtained the best prediction models for different water quality parameters. The Lasso model has a [Formula: see text] value above 0.8 for water body organic pollutants COD, TN, and TP, and the decrease value for [Formula: see text] and [Formula: see text] is below 0.1, which indicates that the model has high prediction accuracy and strong generalization ability, but the results of SS and NH₄-N do not meet the expected accuracy. In the inversion model of RF for COD, [Formula: see text] is higher than [Formula: see text], which shows excellent performance, and has certain prediction ability for SS and NH₄-N. The RF model and Lasso model complement each other effectively in applicability and prediction accuracy. Compared with the traditional regression model PLS, machine learning has obvious overall advantages, making it more suitable for classified inversion prediction of urban river water quality parameters.