Defined as any substance in the air that may harm humans, animals, vegetation, and materials, air pollution poses a great danger to human health. It has turned into a worldwide problem as well as a huge environmental risk. Recent years have witnessed the increase of air pollution in many cities around the world. Similarly, it has become a big problem in Iran. Although ground-level monitoring can provide accurate PM2.5 measurements, it has limited spatial coverage and resolution. As a result, Satellite Remote Sensing (RS) has emerged as an approach to estimate ground-level ambient air pollution, making it possible to monitor atmospheric particulate matters continuously and have a spatial coverage of them. Recent studies show a high correlation between ground level PM2.5, estimated by RS on the one hand, and measurements, collected at regulatory monitoring sites on the other. As such, the present study addresses the relation between air pollution and satellite images. For so doing, it derives RS estimates, using satellite measurements from Landsat satellite images. Monitoring data is the daily concentration of PM2.5 contaminants, obtained from air pollution stations. The relation between the concentration of pollutants and the values of various bands of Landsat satellite images is examined through 19 regression models. Among them, the Ensembles Bagged Trees has the lowest Root-Mean-Square Error (RMSE), equal to 21.88. Results show that this model can be used to estimate PM2.5 contaminants, based on Landsat satellite images.

This study aims to examine the ability of free floating aquatic plants to remove phosphorus and to predict the reduction of phosphorus from rice mill wastewater using soft computing techniques. A mesocosm study was conducted at the mill premises under normal conditions, and reliable results were obtained. Four aquatic plants, namely water hyacinth, water lettuce, salvinia, and duckweed were used for this study. The growth of all the plants was inhibited in rice mill wastewater due to low pH, high chemical oxygen demand, high conductivity, and high phosphorus concentration. Subsequently, a 1:1 ratio of mill water to tap water was used. A control was maintained to assess the aquatic plant technology. In this study, the aquatic plants reduced the total phosphorus content up to 80 % within 15 days. A comparison between three modeling techniques e.g. Artificial neural network (ANN), Random forest (RF) and M5P has been done considering the reduction rate of total phosphorus as predicted variable. In this paper, the data set has been divided in two parts, 70 % is used to train the model and residual 30 % is used for testing of the model. Artificial neural network shows promising results as compared to random forest and M5P tree modelling. The root mean square error (RMSE) for all the three models is observed as 0.0162, 0.0204 and 0.0492 for ANN, RF and M5P tree, respectively.

International audience | Preservation of biodiversity and ecosystem services is critical for sustainable development and human well-being. However, an unprecedented erosion of biodiversity is observed and the use of plant protection products (PPP) has been identified as one of its main causes. In this context, at the request of the French Ministries responsible for the Environment, for Agriculture and for Research, a panel of 46 scientific experts ran a nearly 2-year-long (2020-2022) collective scientific assessment (CSA) of international scientific knowledge relating to the impacts of PPP on biodiversity and ecosystem services. The scope of this CSA covered the terrestrial, atmospheric, freshwater, and marine environments (with the exception of groundwater) in their continuity from the site of PPP application to the ocean, in France and French overseas territories, based on international knowledge produced on or transposable to this type of context (climate, PPP used, biodiversity present, etc.). Here, we provide a brief summary of the CSA's main conclusions, which were drawn from about 4500 international publications. Our analysis finds that PPP contaminate all environmental matrices, including biota, and cause direct and indirect ecotoxicological effects that unequivocally contribute to the decline of certain biological groups and alter certain ecosystem functions and services. Levers for action to limit PPP-driven pollution and effects on environmental compartments include local measures from plot to landscape scales and regulatory improvements. However, there are still significant gaps in knowledge regarding environmental contamination by PPPs and its effect on biodiversity and ecosystem functions and services. Perspectives and research needs are proposed to address these gaps.

Antibiotic proliferation in the environment and their persistent nature is an issue of global concern as they induce antibiotic resistance threatening both human health and the ecosystem. Antibiotics have therefore been categorized as emerging pollutants. Fluoroquinolone (FQs) antibiotics are an emerging class of contaminants that are used extensively in human and veterinary medicine. The recalcitrant nature of fluoroquinolones has led to their presence in wastewater, effluents and water bodies. Even at a low concentration, FQs can stimulate antibacterial resistance. The main sources of FQ contamination include waste from pharmaceutical manufacturing industries, hospitals and households that ultimately reaches the wastewater treatment plants (WWTPs). The conventional WWTPs are unable to completely remove FQs due to their chemical stability. Therefore, the development and implementation of more efficient, economical, convenient treatment and removal technologies are needed to adequately address the issue. This review provides an overview of the technologies available for the removal of fluoroquinolone antibiotics from wastewater including adsorptive removal, advanced oxidation processes, removal using non-carbon based nanomaterials, microbial degradation and enzymatic degradation. Each treatment technology is discussed on its merits and limitations and a comparative view is presented on the choice of an advanced treatment process for future studies and implementation. A discussion on the commercialization potential and eco-friendliness of each technology is also included in the review. The importance of metabolite identification and their residual toxicity determination has been emphasized. The last section of the review provides an overview of the policy interventions and regulatory frameworks that aid in retrofitting antibiotics as a central key focus contaminant and thereby defining the discharge limits for antibiotics and establishing safe manufacturing practices.

Cryptosporidium and Giardia are important parasites due to their zoonotic potential and impact on human health, often causing waterborne outbreaks of disease. Detection of (oo)cysts in water matrices is challenging and few countries have legislated water monitoring for their presence. The aim of this study was to investigate the presence and origin of these parasites in different water sources in Northern Greece and identify interactions between biotic/abiotic factors in order to develop risk-assessment models. During a 2-year period, using a longitudinal, repeated sampling approach, 12 locations in 4 rivers, irrigation canals, and a water production company, were monitored for Cryptosporidium and Giardia, using standard methods. Furthermore, 254 faecal samples from animals were collected from 15 cattle and 12 sheep farms located near the water sampling points and screened for both parasites, in order to estimate their potential contribution to water contamination. River water samples were frequently contaminated with Cryptosporidium (47.1%) and Giardia (66.2%), with higher contamination rates during winter and spring. During a 5-month period, (oo)cysts were detected in drinking-water (<1/litre). Animals on all farms were infected by both parasites, with 16.7% of calves and 17.2% of lambs excreting Cryptosporidium oocysts and 41.3% of calves and 43.1% of lambs excreting Giardia cysts. The most prevalent species identified in both water and animal samples were C. parvum and G. duodenalis assemblage AII. The presence of G. duodenalis assemblage AII in drinking water and C. parvum IIaA15G2R1 in surface water highlights the potential risk of waterborne infection. No correlation was found between (oo)cyst counts and faecal-indicator bacteria. Machine-learning models that can predict contamination intensity with Cryptosporidium (75% accuracy) and Giardia (69% accuracy), combining biological, physicochemical and meteorological factors, were developed. Although these prediction accuracies may be insufficient for public health purposes, they could be useful for augmenting and informing risk-based sampling plans.

Pesticides are widely and intensively used in the world for crops protection. High pesticide loadings can potentially pollute the water resource. However, little is known about the usage, environmental emission and fate of pesticides in river basins. Here, we firstly established a pesticide emission estimation method, and investigated the environmental fate of three commonly used pesticides (chlorpyrifos, triazophos, and isoprothiolane) in Dongjiang River basin, southern China using mathematical modelling approach in combination with field monitoring. The distributed hydrological model SWAT (Soil and Water Assessment Tool) was applied to model the emission of the target pesticides from farmland to stream water, and their fate in the basin. A satisfactory model calibration for flow and suspended sediment was obtained based on eight-year observation data of four hydrological monitoring stations in Dongjiang River basin. The differences between the simulation and observation of pesticides were almost within an order of magnitude, including more than 53% differences within 0.5 order of magnitude. In the river basin, 78860 kg of chlorpyrifos, 54990 kg of triazophos and 35320 kg of isoprothiolane were sprayed onto the crops, the estimated annual emissions of the basin come up to 1801 kg, 3779 kg, and 2330 kg under the conditions of rainfall, surface runoff and percolation. After a series of environmental processes including settlement and degradation within the channels, the predicted export masses for chlorpyrifos, triazophos and isoprothiolane were reduced to 266 kg, 1858 kg, 1350 kg, respectively. Successful prediction suggests that the reliable estimation method combing the SWAT modelling can help us understand the source, concentration levels and fate of pesticides in river basin in different scales. Combing the method of emission and fate modelling method we proposed, countries and regions lacking pesticide-application database can facilitate better management of pesticides.

Thiocyanate (SCN⁻)-based autotrophic denitrification (AD) has recently been demonstrated as a promising technology that could be integrated with anaerobic ammonium oxidation (Anammox) to achieve simultaneous removal of nitrogen and SCN⁻. However, there is still a lack of a complete SCN⁻-based AD model, and the potential microbial competition/synergy between AD bacteria and Anammox bacteria under different operating conditions remains unknown, which significantly hinders the possible application of coupling SCN⁻-based AD with Anammox. To this end, a complete SCN⁻-based AD model was firstly developed and reliably calibrated/validated using experimental datasets. The obtained SCN⁻-based AD model was then integrated with the well-established Anammox model and satisfactorily verified with experimental data from a system coupling AD with Anammox. The integrated model was lastly applied to investigate the impacts of influent NH₄⁺-N/NO₂⁻-N ratio and SCN⁻ concentration on the steady-state microbial composition as well as the removal of nitrogen and SCN⁻. The results showed that the NH₄⁺-N/NO₂⁻-N ratio in the presence of a certain SCN⁻ level should be controlled at a proper value so that the maximum synergy between AD bacteria and Anammox bacteria could be achieved while their competition for NO₂⁻ would be minimized. For the simultaneous maximum removal (>95%) of nitrogen and SCN⁻, there existed a negative relationship between the influent SCN⁻ concentration and the optimal NH₄⁺-N/NO₂⁻-N ratio needed.

In indoor air, terpene-ozone reactions can form secondary organic aerosols (SOA) in a transient process. ‘Real world’ measurements conducted in a furnished room without air conditioning were modelled involving the indoor background of airborne particulate matter, outdoor ozone infiltrated by natural ventilation, repeated transient limonene evaporations, and different subsequent ventilation regimes. For the given setup, we disentangled the development of nucleated, coagulated, and condensed SOA fractions in the indoor air and calculated the time dependence of the aerosol mass fraction (AMF) by means of a process model. The AMF varied significantly between 0.3 and 5.0 and was influenced by the ozone limonene ratio and the background particles which existed prior to SOA formation. Both influencing factors determine whether nucleation or adsorption processes are preferred; condensation is strongly intensified by particulate background. The results provide evidence that SOA levels in natural indoor environments can surpass those known from chamber measurements. An indicator for the SOA forming potential of limonene was found to be limona ketone. Multiplying its concentration (in μg/m³) by 450(±100) provides an estimate of the concentration of the reacted limonene. This can be used to detect a high particle formation potential due to limonene pollution, e.g. in epidemiological studies considering adverse health effects of indoor air pollutants.