Recycling of electric and electronic waste products (e-waste) which amounted to more than 50 million metric tonnes per year worldwide is a massive and global operation. Unfortunately, an estimated 70-80% of this waste has not been properly managed because the waste went from developed to low-income countries to be dumped into landfills or informally recycled. Such recycling has been carried out either directly on landfill sites or in small, often family-run recycling shops without much regulations or oversights. The process traditionally involved manual dismantling, cleaning with hazardous solvents, burning and melting on open fires, etc., which would generate a variety of toxic substances and exposure/hazards to applicators, family members, proximate residents and the environment. The situation clearly calls for global responsibility to reduce the impact on human health and the environment, especially in developing countries where poor residents have been shouldering the hazardous burden. On the other hand, formal e-waste recycling has been mainly conducted in small scales in industrialised countries. Whether the latter process would impose less risk to populations and environment has not been determined yet. Therefore, the main objectives of this review are: 1. to address current trends and emerging threats of not only informal but also formal e-waste management practices, and 2. to propose adequate measures and interventions. A major recommendation is to conduct independent surveillance of compliance with e-waste trading and processing according to the Basel Ban Amendment. The recycling industry needs to be carefully evaluated by joint effort from international agencies, producing industries and other stakeholders to develop better processes. Subsequent transition to more sustainable and equitable e-waste management solutions should result in more effective use of natural resources, and in prevention of adverse effects on health and the environment.

Ambient air pollution is likely a risk factor for asthma, and recent evidence suggests the possible relevance of road traffic noise. We examined the associations of long-term exposure to air pollution and road traffic noise with adult-asthma incidence. We followed 28,731 female nurses (age > 44 years) from the Danish Nurse Cohort, recruited in 1993 and 1999, for first hospital contact for asthma from 1977 until 2015. We estimated residential annual mean concentrations of particulate matter with diameter < 2.5 µm (PM_2.5) since 1990 and nitrogen dioxide (NO₂) since 1970 with the Danish DEHM/UBM/AirGIS modeling system, and road traffic noise (L_den) since 1970 with the Nord2000 model. Time-varying Cox regression models were used to associate air pollution and road traffic noise exposure with asthma incidence. During 18.6 years' mean follow-up, 528 out of 23,093 participants had hospital contact for asthma. The hazard ratios (HR) and 95% confidence intervals for asthma incidence associated with 3-year moving average exposures were 1.29 (1.03, 1.61) per 6.3 µg/m³ for PM_2.5, 1.16 (1.07, 1.27) per 8.2 µg/m³ for NO₂, and 1.12 (1.00, 1.25) per 10 dB for L_den. The HR for NO₂ remained unchanged after adjustment for either PM_2.5 or L_den, while the HRs for PM_2.5 and L_den attenuated to unity after adjustment for NO₂. Long-term exposure to air pollution was associated with adult-asthma incidence independently of road traffic noise, with NO₂ most relevant. Road traffic noise was not independently associated with adult-asthma incidence.

Changes in air pollution in the region of the city of Novi Sad due to the COVID-19 induced state of emergency were evaluated while using data from permanently operating air quality monitoring stations belonging to the national, regional, and local networks, as well as ad hoc deployed low-cost particulate matter (PM) sensors. The low-cost sensors were collocated with reference gravimetric pumps. The starting idea for this research was to determine if and to what extent a massive change of anthropogenic activities introduced by lockdown could be observed in main air pollutants levels. An analysis of the data showed that fine and coarse particulate matter, as well as SO₂ levels, did not change noticeably, compared to the pre-lockdown period. Isolated larger peaks in PM pollution were traced back to the Aralkum Desert episode. The reduced movement of vehicles and reduced industrial and construction activities during the lockdown in Novi Sad led to a reduction and a more uniform profile of the PM_2.5 levels during the period between morning and afternoon air pollution peak, approximately during typical working hours. Daily profiles of NO₂, NO, and NO_X during the state of emergency proved lower levels during most hours of the day, due to restrictions on vehicular movement. CO during the state of the emergency mainly exhibited a lower level during night. Pollutants having transportation-dominated source profiles exhibited a decrease in level, while pollutants with domestic heating source profiles mostly exhibited a constant level. Considering local sources in Novi Sad, slight to moderate air quality improvement was observed after the lockdown as compared with days before. Furthermore, PM low-cost sensors’ usefulness in air quality assessment was confirmed, as they increase spatial resolution, but it is necessary to calibrate them at the deployment location.

Accurate monitoring of water temperature (T), electrical conductivity (EC), and dissolved oxygen (DO) is essential for assessing river health, yet conventional methods are often limited by cost, coverage, and data latency. This study proposes an integrated framework that combines Sentinel-2 satellite imagery with machine learning (ML) models to enhance large-scale, real-time water quality monitoring. Five ML algorithms – deep neural networks, eXtreme gradient boosting, Kolmogorov–Arnold networks, long short-term memory (LSTM), and temporal Kolmogorov–Arnold networks – were evaluated using daily time-series data for the Danube River at Novi Sad from October 2012 to December 2023. Model interpretability was ensured through Shapley additive explanations and LIME. LSTM achieved the highest accuracy for temperature prediction (R2 = 0.97, RMSE = 1.45 °C, standard error = 1.31 °C), while XGBoost outperformed others for dissolved oxygen (R2 = 0.79). High accuracy was also observed for conductivity predictions (LSTM, R2 = 0.90). Seasonal variables and spectral bands (B8a, B4, B11) were found to be dominant predictors. The novelty of this work lies in the fusion of temporal deep learning, explainable AI, and multispectral satellite data for interpretable, scalable, and cost-effective water quality assessment. This approach supports timely water quality degradation and decision-making in river basin management.

The aim of this work was to determine the most relevant pollutants in Danube surface water and wastewater in the city of Novi Sad, and to conduct a risk assessment approach on substances for the optimization of future monitoring programmes. According to the requirements of Serbian law, obligatory and operating monitoring was focused on physico-chemical and biological parameters while the expanded monitoring programmes have not been applied – often due to practical circumstances. Novi Sad, with a population of more than 300.000 inhabitants, does not have a wastewater treatment plant, and about 2 m³ of wastewater is discharged directly to the Danube river every day. Screening analyses of the water within the Danube basin around city of Novi Sad included more than 300 different organic substances, while target analysis was conducted for all WFD pollutants. The methodology for generating the list of priority substances was applied for the first time in the Danubian region around the city of Novi Sad. By applying the prioritization procedure, 86 organic substances were determined in screening analyses and 27 substances obtained within target analyses were identified as the most relevant. In addition, seasonal variation analysis was conducted to determine the occurrence trends of specific compounds in different seasons.