In recent years, implementation of aggressive and strict clean air policies has resulted in significant decline in observed PM₂.₅ concentration in the Beijing–Tianjin–Hebei (BTH) region and its surrounding areas (i.e., the “2 + 26” region). To eliminate the effects of interannual and seasonal meteorological variation, and to evaluate the effectiveness of emission abatement policies, we applied a boosted regression tree model to remove confounding meteorological factors. Results showed that the annual average PM2.5 concentration normalized by meteorology for the “2 + 26” region declined by 38% during 2014–2019 (i.e., from 96 to 60 μg/m³); however, the BTH region exhibited the most remarkable decrease in PM₂.₅ concentration (i.e., a 60% reduction). Certain seasonal trend in normalized PM₂.₅ level remained for four target subregions owing to the effects of anthropogenic emissions in autumn and winter. Although strong interannual variations of meteorological conditions were unfavorable for pollutant dispersion during the heating seasons of 2016–2018, the aggressive abatement policies were estimated to have contributed to reductions in normalized PM₂.₅ concentration of 19%, 10%, 19%, and 17% in the BTH, Henan, Shandong, and Shanxi subregions, respectively. Our study eliminated the meteorological contribution to concentration variation and confirmed the effectiveness of the implemented clean air policies.

In predicting palm oil mill effluent (POME) degradation efficiency, previous developed quadratic model quantitatively evaluated the effects of O2 flowrate, TiO2 loadings and initial concentration of POME in labscale photocatalytic system, which however suffered from low generalization due to the overfitting behaviour. Evidently, high RMSE (131.61) and low R₂ (−630.49) obtained indicates its insufficiency in describing POME degradation at unseen factor ranges, hence verified the fact of poor generalization. To overcome this issue, several models were developed via machine learning-assisted techniques, namely Gaussian Process Regression (GPR), Linear Regression (LR), Decision Tree (DT), Supported Vector Machine (SVM) and Regression Tree Ensemble (RTE), subsequently being assessed systematically. To achieve high generalization, all models were subjected to ‘train-all-test-all’ strategy, 5-fold and 10-fold cross validation. Specifically, GPR model was furnished with high accuracy in ‘train-all-test-all’ strategy, judging from its low RMSE (1.0394) and high R₂ (0.9962), which however menaced by the risk of overfitting. In contrast, despite relatively poorer RMSE and R₂ (1.7964 and 0.9886) obtained in 5-fold cross validation, GPR model was rendered with highest generalization, while sufficiently preserving its accuracy in development process. Besides, SVM and RTE models were also demonstrated promising R₂ (0.9372 and 0.9208), which however shadowed by their high RMSEs (4.2174 and 4.7366). Furthermore, the extraordinary generalization of GPR model was coincidentally verified in 10-fold cross validation. The lowest RMSE (2.1624) and highest R₂ (0.9835) obtained with feature number of 36 asserted its sufficiency in both generalization and accuracy prospect. Other models were all rendered with slight lower R₂ (> 0.9), plausibly due to the higher RMSE (> 4.0). According to GPR model, optimized POME degradation (52.52%) can be obtained at 70 mL/min of O₂, 70.0 g/L of TiO₂ and 250 ppm of POME concentration, with only ∼3% error as compared to the actual data.

Both air pollution and dyslipidemias contributed to large number of deaths and disability-adjusted life lost years. Long-term air pollution exposure was related to changed blood lipids and risk of dyslipidemias. This study was designed to evaluate relationships between air pollutants, blood lipids and prevalence of dyslipidemias in a Chinese rural population exposed to high-level air pollution based on baseline data of The Henan Rural Cohort study. An amount of 39,057 participants from rural areas in China were included. The 3-year average exposure of air pollutants (PM2.5, PM10, NO2) was estimated by a spatiotemporal model. Logistic and linear regression models were employed to explore relationships between air pollutants, blood lipids (TC, TG, HDL-C and LDL-C) and prevalence of dyslipidemias. The three-year concentration of PM2.5, PM10 and NO2 was 72.8 ± 2.3 μg/m3, 131.5 ± 5.7 μg/m3and 39.1 ± 3.1 μg/m3, respectively. Overall, increased air pollution exposure was related to increased TC and LDL-C, while decreased TG and HDL-C. Each 1-μg/m3 increment of PM2.5 was related to 0.10% (0.07%–0.19%) increase in TC, 0.63% (0.50%–0.77%) increase in LDL-C, 2.93% (2.70%–3.16%) decrease in TG, 0.49% (0.38%–0.60%) decrease in HDL-C; and 5.7% (95%CI: 3.7%–7.6%), 4.0% (95%CI: 2.1%–6.0%) and 3.8% (95%CI: 2.5%–5.1%) increase in odds for hypercholesterolemia, hyperbetalipoproteinemia and hypoalphalipoproteinemia, respectively. Stronger associations were found in male and older participants. Findings suggest that air pollutants were associated with changed blood lipid levels and higher risk of dyslipidemias among rural population. Male and elder people should pay more attention to personal safety protection.

Northern China is a significant source of dust source in Central Asia. Thus, high-resolution analysis of dust storms and comparison of dust sources in different regions of northern China are important to clarify the formation mechanism of East Asian dust storms and predict or even prevent such storms. Here, we analyzed spatiotemporal trends in dust storms that occurred in three main dust source regions during 1960–2007: Taklimakan Desert (western region [WR]), Badain Jaran and Tengger Deserts (middle region [MR]), and Otindag Sandy Land (eastern region [ER]). We analyzed daily dust storm frequency (DSF) at the 10-day scale (first [FTDM], middle [MTDM], and last [LTDM] 10 days of a month), and investigated the association of dust storm occurrences with meteorological factors. The 10-day DSF was greatest in the FTDM (accounting for 77.14% of monthly occurrences) in the WR, MTDM (45.85%) in the MR, and LTDM (72.12%) in the ER, showing a clear trend of movement from the WR to the ER. Temporal analysis of DSF revealed trend changes over time at annual and 10-day scales, with mutation points at 1985 and 2000. We applied single-factor and multiple-factor analyses to explore the driving mechanisms of DSF at the 10-day scale. Among single factors, a low wind-speed threshold, high solar radiation, and high evaporation were correlated with a high DSF, effectively explaining the variations in DSF at the 10-day scale; however, temperature, relative humidity, and precipitation poorly explained variations in DSF. Similarly, multiple-factor analysis using a classification and regression tree revealed that maximum wind speed was a major influencing factor of dust storm occurrence at the 10-day scale, followed by relative humidity, evaporation, and solar radiation; temperature and precipitation had weak influences. These findings help clarify the mechanisms of dust storm occurrence in East Asia.

Eutrophic freshwater lake ecosystems are receiving increasing public attention due to a global increase in large-scale harmful cyanobacterial blooms in surface waters. However, the contribution of phytodetritus accumulation in benthic sediments post-bloom remains unclear. In this study, field investigations were performed using microsensors to evaluate benthic phytodetritus mats by measuring TOC/TN ratios, pigments, biodegradable compounds and odorants as descriptive parameters. Results show that the massive amount of phytodetritus trapped by aquatic plants gradually evolved into benthic cyanobacterial detritus mats, which were characterized as anoxic, reductive and low pH. It was confirmed that the occurrence of odorants is more serious in the detritus mats due to decay and decomposition of the accumulated phytodetritus. The mean odorant content in the vegetated zones was 3–52 times higher than that in the unvegetated zones. The dominant odorants were dimethyl trisulfide (DMTS), β-ionone and β-cyclocitral, with mean contents of 52.38 ng·(g·dw)-1, 162.20 ng·(g·dw)-1 and 307.51 ng·(g·dw)-1, respectively, in the sediment. In addition, odorant production appears to be associated with the distribution of biodegradable compounds in the sediment. This is supported by the marked correlation observed between biodegradable compounds and odorants. Multiple regression analysis showed that biodegradable compounds can be used as indicators to predict odorant content in the sediment. It is noteworthy that the odorant trend in the water column and sediment is symmetrical, indicating a risk of diffusion from the sediment to the water column. This study helps to clarifying the contributions of benthic cyanobacterial detritus mats to odorant production in shallow eutrophic lakes. The information provided herein may also be useful for future management of aquatic ecosystems.

Scots pine (Pinus sylvestris L.) is a widespread tolerant forest tree-species; however, its adaptability to environmental change differs among sites with various buffering capacity. In this study, we compared the spatial effects of aridity index (AI) and nitrogen deposition (ND) on biomass density in natural and man-made pine stands of differing soil fertility using geographically weighted multiple lag regression. Soil fertility was defined using soil series as zonal trophic (27.9%), acidic (48.2%), gleyed (15.2%) and as azonal exposed (2.5%), maple (2.4%), ash (0.8%), wet (2.1%) and peat (0.9%) under pine stands in the Czech Republic (Central Europe; 4290.5 km²; 130–1298 m a.s.l.). Annual AI and ND in every pine stand were estimated by intersection between raster and vector from 1 × 1 km grid for years 2000, 2003, 2007 and 2010 of severe non-specific forest damage spread. Biomass density was obtained from a MODIS 250 × 250 m raster using the enhanced vegetation index (EVI) for years 2000–2015, with a decrease in EVI indicating non-specific damage. Environmental change was assessed by comparing predictor values at EVI time t and t+λ. Non-specific damage was registered over 51.9% of total forest area. Less than 8.8% of damaged stands were natural and the rest (91.2%) of damaged stands were man-made. Pure pine stands were more damaged than mixed. The ND effect prevailed up to 2007, while AI dominated later. Temporal increasing ND effect under AI effectiveness led to the most significant pine stand damage in 2008 and 2014. Predictors from 2000 to 2007 afflicted 58.5% of non-specifically damaged stands at R² 0.09–0.76 (median 0.38), but from 2000 to 2010 afflicted 57.1% of the stands at R² 0.16–0.75 (median 0.40). The most damaged stands occurred on acidic sites. Mixed forest and sustainable management on natural sites seem as effective remediation reducing damage by ND.

Chronic exposure to environment noise is associated with sleep disturbance and cardiovascular diseases. Assessment of population exposed to environmental noise is limited by a lack of routine noise sampling and is critical for controlling exposure and mitigating adverse health effects. Land use regression (LUR) model is newly applied in estimating environmental exposures to noise. Machine-learning approaches offer opportunities to improve the noise estimations from LUR model. In this study, we employed random forests (RF) model to estimate environmental noise levels in five Canadian cities and compared noise estimations between RF and LUR models. A total of 729 measurements and 33 built environment-related variables were used to estimate spatial variation in environmental noise at the global (multi-city) and local (individual city) scales. Leave one out cross-validation suggested that noise estimates derived from the RF global model explained a greater proportion of variation (R2: RF = 0.58, LUR = 0.47) with lower root mean squared errors (RF = 4.44 dB(A), LUR = 4.99 dB(A)). The cross-validation also indicated the RF models had better general performance than the LUR models at the city scale. By applying the global models to estimate noise levels at the postal code level, we found noise levels were higher in Montreal and Longueuil than in other major Canadian cities.

Soils have the ability to modify contaminant bioavailability and toxicity. Prediction the modifying effect of soil on arsenic phytoaccumulation and phytoavailability using either soil property data or soil chemical extraction data in risk assessment of contaminated soil is highly desirable. In this study, plant bioassays important to ecological receptors, were conducted with 20 soils with a wide range in chemical and physical soil properties to determine the relationships between As measured by soil chemical extraction (soil pore water, Bray-1, sodium phosphate solution, hydroxylamine hydrochloride, and acid ammonium oxalate) or soil physico/chemical properties on arsenic phytotoxicity and phytoaccumulation. Soil pore water As and Bray-1 extracted As were significantly (P < 0.01) correlated with lettuce tissue As and those extractants and sodium phosphate were correlated with ryegrass tissue As. Hydroxylamine and acid ammonium oxalate extractions did not correlate with plant bioassay endpoints. Simple regression results showed that lettuce tissue relative dry matter growth (RDMG) was inversely related to tissue As concentration (r² = 0.85, P < 0.01), with no significant relationship for ryegrass. Soil clay exhibited strong adsorption for As and significantly reduce tissue As for lettuce and ryegrass. In addition to clay content, reactive aluminum oxide (AlOx), reactive Fe oxide (FeOx) and eCEC was inversely related to ryegrass tissue As. Multiple regression equation was strongly predictive (r² = 0.83) for ryegrass tissue As (log transformed) using soil AlOx, organic matter, pH, and eCEC as variables. Soil properties can greatly reduce contaminant phytoavailability, plant exposure and risk, which should be considered when assessing contaminant exposure and site-specific risk in As-contaminated soils.

The understanding for the impact of petrochemical pollutants exposure on renal functions is limited.Our study examined the associations between renal functions and pollutants exposure in adult residents living in the vicinity of a petrochemical industry.We recruited 2069 adult residents near a big petrochemical complex in Taiwan in 2009–2012, and they were categorized into high exposure (HE) and low exposure (LE) groups based on their address to source by 10 km radius. Study subjects were measured the urinary levels of arsenic, cadmium, mercury, thallium, and 1-hydroxypyrene (1-OHP). The estimated glomerular filtration rate (eGFR) was calculated using the Taiwanese Chronic Kidney Disease Epidemiology Collaboration equation, and the chronic kidney disease (CKD) prevalence and risks were defined according to KDIGO 2012 guidelines. Adjusted generalized linear and logistic regression models were applied to evaluate the associations between petrochemical exposure and renal functions.Subjects in the HE areas had significantly lower eGFR, higher CKD prevalence, and higher levels of urinary arsenic, cadmium, mercury, thallium and 1-OHP. The closer to complex and high exposure group of study subjects were significantly associated with the decrease in eGFR, higher ORs for CKD and high-intermediate risk of CKD. In addition, the study subjects who had two-fold urinary arsenic and 1-OHP levels were significantly with decreased 0.68 and 0.49 ml/min/1.73 m2 of eGFR, respectively.Residing closer and higher arsenic and polycyclic aromatic hydrocarbon exposure were associated with the renal impairment and risks of CKD among the residential population near the petrochemical industry.

Most of the previous researches estimate influencing factors impact on air quality average without considering the heterogeneity of influential factors on different levels of air quality. In order to detect the different effects of influencing factors on air quality index (AQI) between lower-AQI and higher-AQI cities, this study applies a spatial quantile regression model (SQRM) to investigate heterogeneity of influential factors on AQI, while accounting for spatial autocorrelation of AQI. The results show that heterogeneity effects of windspeed, terrain slope, urbanization sprawl and spatial autocorrelation on AQI are large across the entire AQI spectrum, while heterogeneity effects of precipitation, temperature, relative humidity, terrain fluctuation and urbanization intensity on AQI are not obvious. The spatial positive autocorrelation of AQI in higher-AQI cities is greater than that in lower-AQI cities. Compared with higher-AQI cities, the negative impact of terrain slope on AQI is lager in lower-AQI cities. One unit increase in wind speed contributes AQI to decrease 9.31 to 5.64 then to 5.39 for lower, medium and higher-AQI cities. One unit increase in urbanization sprawl would lead AQI increase 25.6 to 15.6 then to 10.5 for lower, medium and higher-AQI cities. The heterogeneity analysis of meteorological, topographic and socioeconomic factors effects on air quality are of guiding significance for realizing the differentiation of policy measures for air pollution prevention and control.