With the increase in population and industrialization, air pollution has become one of the global problems nowadays. Therefore, air pollutant parameters should be measured at regular intervals, and the necessary measures should be taken by evaluating the results of measurements. In order to prevent air pollution, pollutant parameters must be evaluated within the framework of a model. Recently, in order to obtain objective and more sensitive results with regard to air pollution nowadays, studies, which use machine learning algorithms in artificial intelligence technologies, have been carried out. In this study, PM₁₀ concentrations, which are obtained from 7 stations in Ankara province in Turkey, were trained with machine learning algorithms (LASSO, SVR, RF, kNN, xGBoost, ANN). The PM₁₀ concentrations of the years 2009–2017 of 6 stations in Ankara were given as input, and the PM₁₀ concentrations of the seventh station for the year 2018 were predicted. The model development stage was repeated for each station, and the performance and error rates of the algorithms were determined by comparing the results produced by the algorithms with the actual results. The best results were provided with ANN (R² = 0.58, RMSE = 20.8, MAE = 14.4). The spatial distribution of the estimated concentration results was provided through Geographic Information System (GIS), and spatial strategies for improving air pollution over land use were established.

Air pollution can increase the risk of respiratory diseases, enhancing the susceptibility to viral and bacterial infections. Some studies suggest that small air particles facilitate the spread of viruses and also of the new coronavirus, besides the direct person-to-person contagion. However, the effects of the exposure to particulate matter and other contaminants on SARS-CoV-2 has been poorly explored. Here we examined the possible reasons why the new coronavirus differently impacted on Italian regional and provincial populations. With the help of artificial intelligence, we studied the importance of air pollution for mortality and positivity rates of the SARS-CoV-2 outbreak in Italy. We discovered that among several environmental, health, and socio-economic factors, air pollution and fine particulate matter (PM2.5), as its main component, resulted as the most important predictors of SARS-CoV-2 effects. We also found that the emissions from industries, farms, and road traffic - in order of importance - might be responsible for more than 70% of the deaths associated with SARS-CoV-2 nationwide. Given the major contribution played by air pollution (much more important than other health and socio-economic factors, as we discovered), we projected that, with an increase of 5–10% in air pollution, similar future pathogens may inflate the epidemic toll of Italy by 21–32% additional cases, whose 19–28% more positives and 4–14% more deaths. Our findings, demonstrating that fine-particulate (PM2.5) pollutant level is the most important factor to predict SARS-CoV-2 effects that would worsen even with a slight decrease of air quality, highlight that the imperative of productivity before health and environmental protection is, indeed, a short-term/small-minded resolution.

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.

Most laboratory studies have focused on the effects of nanoplastics instead of plastics at the micrometer scale, which are the major microplastics (MPs) discarded in marine environments. Knowledge on the potential effects of micrometer scale plastics on marine microalgae remains limited. It remains unknown whether the micrometer scale plastics also affect microalgal growth, lipid accumulation and resistance to organic contaminants? In addition, the role of polymer-size on the potential hazardous effects of MPs on microalgae is unknown. In the present study, cell populations of a marine diatom, Phaeodactylum tricornutum, were treated with micrometer scale polyethylene (PEMP, 150 μm) and unplasticized polyvinyl chloride (uPVCMP, 250 μm) powders in the laboratory. Growth was assessed using a hemacytometer and neutral lipid concentrations were evaluated using the Nile Red staining method under short-term (four days) and long-term (nine days) exposure. The effects of combined PEMP and phenanthrene (Phe), and uPVCMP and Phe exposures over four days on growth were investigated. Importance scores and SHapley Additive exPlanations (SHAP) values were calculated to assess the contributions of seven factors in exposure systems to the hazardous effects of MPs on microalgae using a machine-learning prediction based on 165 data sets. Both MP types did not influence algal growth and lipid accumulation but minimized algal inhibition by the action of Phe at four days. In addition, lipid accumulation was induced at nine days. Both importance scores and SHAP values indicated that MP polymer-size was the key factor influencing MP toxicity in microalgae. In conclusion, MPs had adverse effects only in chronic tests and the potential adsorption of MPs could have led to the lower levels of toxicity in a combined MP–Phe exposure system. Compared to nanoplastics, MPs in the hundred-micrometer range do not significantly affect growth and their adsorption would not be influenced by size. Therefore, MP size is the most critical factor that should be considered in future laboratory tests and eco-toxicological risk assessments for microalgae.

Mining activities change the chemical composition of the environment and have negative reflection on people’s health and there is no single measure to deal with adverse consequences of mining activities, as each case is specific and needs to be understood and mitigated in a unique way. In this study, the combination of compositional data analysis (CoDA), k-means algorithm, hierarchical cluster analysis applied to reveal the geochemical associations of potentially toxic elements (PTE) in soil of Alaverdi city (Armenia) (Ti, Fe, Ba, Mn, Co, V, Pb, Zn, Cu, Cr, Mo, As). Additionally, to assess PTE-induced health risk, two commonly used approaches were used. The obtained results show that the combination of CoDA and machine learning algorithms allow to identify and describe three geochemical associations of the studied elements: the natural, manmade and hybrid. Moreover, the revealed geochemical associations were linked to the natural pattern of distribution of the element concentrations including the influence of the natural mineralization of the parent rocks, as well as the emission from the copper smelter and urban management related activities. The health risk assessment using the US EPA method demonstrated that the observed contents of studied elements are posing a non-carcinogenic risk to children in the entire territory of the city. In the case of adults, the non-carcinogenic risk was identified in areas situated close to the copper smelter. The Summary pollution index (Zc) values were in line with the results of the US EPA method and indicated that the main residential part of the city was under the hazardous pollution level suggesting the possibility of increase in the overall incidence of diseases among frequently ill individuals, children with chronic diseases and functional disorders of vascular system. The obtained results indicated the need for further in-depth studies with special focus on the synergic effect of PTE.

The prediction and identification of the factors controlling heavy metal transfer in soil-crop ecosystems are of critical importance. In this study, random forest (RF), gradient boosted machine (GBM), and generalised linear (GLM) models were compared after being used to model and identify prior factors that affect the transfer of heavy metals (HMs) in soil-crop systems in the Yangtze River Delta, China, based on 13 covariates with 1822 pairs of soil-crop samples. The mean bioaccumulation factors (BAFs) for all crops followed the order Cd > Zn > As > Cu > Ni > Hg > Cr > Pb. The RF model showed the best prediction ability for the BAFs of HMs in soil-crop ecosystems, followed by GBM and GLM. The R2 values of the RF models for the BAFs of Zn, Cu, Cr, Ni, Hg, Cd, As, and Pb were 0.84, 0.66, 0.59, 0.58, 0.58, 0.51, 0.30, and 0.17, respectively. The primary controlling factor in soil-to-crop transfer of all HMs under study was plant type, followed by soil heavy metal content and soil organic materials. The model used herein could be used to assist the prediction of heavy metal contents in crops based on heavy metal contents in soil and other covariates, and can significantly reduce the cost, labour, and time requirements involved with laboratory analysis. It can also be used to quantify the importance of variables and identify potential control factors in heavy metal bioaccumulation in soil-crop ecosystems.

Fine particulate matter (PM₂.₅) has been recognized as a key air pollutant that can influence population health risk, especially during extreme cases such as wildfires. Previous studies have applied geospatial techniques such as land use regression to map the ground-level PM₂.₅, while some recent studies have found that Aerosol Optical Depth (AOD) derived from satellite images and machine learning techniques may be two elements that can improve spatiotemporal prediction. However, there has been a lack of studies evaluating use of different machine learning techniques with AOD datasets for mapping PM₂.₅, especially in areas with high spatiotemporal variability of PM₂.₅.In this study, we compared the performance of eight predictive algorithms with the use of multiple remote sensing datasets, including satellite-derived AOD data, for the prediction of ground-level PM2.5 concentration. Based on the results, Cubist, random forest and eXtreme Gradient Boosting were the algorithms with better performance, while Cubist was the best (CV-RMSE = 2.64 μg/m3, CV-R² = 0.48). Variable importance analysis indicated that the predictors with the highest contributions in modelling were monthly AOD and elevation.In conclusion, appropriate selection of machine learning algorithms can improve ground-level PM2.5 estimation, especially for areas with nonlinear relationships between PM2.5 and predictors caused by complex terrain. Satellite-derived data such as AOD and land surface temperature (LST) can also be substitutes for traditional datasets retrieved from weather stations, especially for areas with sparse and uneven distribution of stations.

Intense harmful algal blooms (HABs) can occur in the backwaters of tributaries supplying large-scale reservoirs. Due to the characteristics of process-based models and difficulties in modelling complex nonlinear processes, traditional models have difficulties disentangling the driving factors of HABs. In this study, we used data-driven methods (i.e., correlation analysis and machine-learning models) to identify the most important drivers of HABs in the Xiangxi River, a tributary of the Three Gorges Reservoir, China (2017–2018), for the dry season (from October to mid-April) and wet season (from April to September). We utilized the maximal information coefficient (MIC) combined with a time lag strategy and prior knowledge to quantitatively identify the driving variables of HABs. An extra trees regression (ETR) model was developed to assess the relative importance of causal variables driving algal blooms for the different periods. The results showed that water temperature was the most important driver for the duration of the study, followed by total nitrogen. Nitrogen had a stronger effect on algal blooms than phosphorus during both the wet and dry seasons. HABs were mainly affected by ammonia nitrogen in the wet season and by other forms of nitrogen in the dry season. In contrast, rather than the water temperature and nutrients, the operation of the Three Gorges Dam (difference between inflow and outflow discharge rate) was the most significant factor for algal blooms during the dry season, but its influence sharply declined during the wet season. This study showed that the key drivers of HABs can differ between seasons and suggests that HAB management should take seasonality into account.

Particulate matter (PM) emission is one of the leading environmental pollution issues associated with the coal mining industry. Before any control techniques can be employed, however, an accurate prediction of PM concentration is desired. Towards this end, this work aimed to provide an accurate estimation of PM concentration using a hybrid machine-learning technique. The proposed predictive model was based on the hybridazation of random forest (RF) model particle swarm optimization (PSO) for estimating PM concentration. The main objective of hybridazing the PSO was to tune the hyper-parameters of the RF model. The hybrid method was applied to PM data collected from an open-cut coal mine in northern China, the Haerwusu Coal Mine. The inputs selected were wind direction, wind speed, temperature, humidity, noise level and PM concentration at 5 min before. The outputs selected were the current concentration of PM₂.₅ (particles with an aerodynamic diameter smaller than 2.5 μm), PM₁₀ (particles with an aerodynamic diameter smaller than 10 μm) and total suspended particulate (TSP). A detailed procedure for the implementation of the RF_PSO was presented and the predictive performance was analyzed. The results show that the RF_PSO could estimate PM concentration with a high degree of accuracy. The Pearson correlation coefficients among the average estimated and measured PM data were 0.91, 0.84 and 0.86 for the PM₂.₅, PM₁₀ and TSP datasets, respectively. The relative importance analysis shows that the current PM concentration was mainly influenced by PM concentration at 5 min before, followed by humidity > temperature ≈ noise level > wind speed > wind direction. This study presents an efficient and accurate way to estimate PM concentration, which is fundamental to the assessment of the atmospheric quality risks emanating from open-cut mining and the design of dust removal techniques.

Little information exists on interaction effects between air pollution and influenza vaccination on allergic respiratory diseases. We conducted a large population-based study to evaluate the interaction effects between influenza vaccination and long-term exposure to ambient air pollution on allergic respiratory diseases in children and adolescents.A cross-sectional study was investigated during 2012–2013 in 94 schools from Seven Northeastern Cities (SNEC) in China. Questionnaires surveys were obtained from 56 137 children and adolescents aged 2–17 years. Influenza vaccination was defined as receipt of the influenza vaccine. We estimated air pollutants exposure [nitrogen dioxide (NO2) and particulate matter with aerodynamic diameters ≤1 μm (PM1), ≤2.5 μm (PM2.5) and ≤10 μm (PM10)] using machine learning methods. We employed two-level generalized linear mix effects model to examine interactive effects between influenza vaccination and air pollution exposure on allergic respiratory diseases (asthma, asthma-related symptoms and allergic rhinitis), after controlling for important covariates.We found statistically significant interactions between influenza vaccination and air pollutants on allergic respiratory diseases and related symptoms (doctor-diagnosed asthma, current wheeze, wheeze, persistent phlegm and allergic rhinitis). The adjusted ORs for doctor-diagnosed asthma, current wheeze and allergic rhinitis among the unvaccinated group per interquartile range (IQR) increase in PM1 and PM2.5 were significantly higher than the corresponding ORs among the vaccinated group [For PM1, doctor-diagnosed asthma: OR: 1.89 (95%CI: 1.57–2.27) vs 1.65 (95%CI: 1.36–2.00); current wheeze: OR: 1.50 (95%CI: 1.22–1.85) vs 1.10 (95%CI: 0.89–1.37); allergic rhinitis: OR: 1.38 (95%CI: 1.15–1.66) vs 1.21 (95%CI: 1.00–1.46). For PM2.5, doctor-diagnosed asthma: OR: 1.81 (95%CI: 1.52–2.14) vs 1.57 (95%CI: 1.32–1.88); current wheeze: OR: 1.46 (95%CI: 1.21–1.76) vs 1.11 (95%CI: 0.91–1.35); allergic rhinitis: OR: 1.35 (95%CI: 1.14–1.60) vs 1.19 (95%CI: 1.00–1.42)]. The similar patterns were observed for wheeze and persistent phlegm. The corresponding p values for interactions were less than 0.05, respectively. We assessed the risks of PM1-related and PM2.5-related current wheeze were decreased by 26.67% (95%CI: 1.04%–45.66%) and 23.97% (95%CI: 0.21%–42.08%) respectively, which was attributable to influenza vaccination (both p for efficiency <0.05).Influenza vaccination may play an important role in mitigating the detrimental effects of long-term exposure to ambient air pollution on childhood allergic respiratory diseases. Policy targeted at increasing influenza vaccination may yield co-benefits in terms of reduced allergic respiratory diseases.