Ozone (O₃) is a potent oxidant associated with adverse health effects. Low-cost O₃ sensors, such as metal oxide (MO) sensors, can complement regulatory O₃ measurements and enhance the spatiotemporal resolution of measurements. However, the quality of MO sensor data remains a challenge. The University of Utah has a network of low-cost air quality sensors (called AirU) that primarily measures PM₂.₅ concentrations around the Salt Lake City valley (Utah, U.S.). The AirU package also contains a low-cost MO sensor ($8) that measures oxidizing/reducing species. These MO sensors exhibited excellent laboratory response to O₃ although they exhibited some intra-sensor variability. Field performance was evaluated by placing eight AirUs at two Division of Air Quality (DAQ) monitoring stations with O₃ federal equivalence methods for one year to develop long-term multiple linear regression (MLR) and artificial neural network (ANN) calibration models to predict O₃ concentrations. Six sensors served as train/test sets. The remaining two sensors served as a holdout set to evaluate the applicability of the new calibration models in predicting O₃ concentrations for other sensors of the same type. A rigorous variable selection method was also performed by least absolute shrinkage and selection operator (LASSO), MLR and ANN models. The variable selection indicated that the AirU’s MO oxidizing species and temperature measurements and DAQ’s solar radiation measurements were the most important variables. The MLR calibration model exhibited moderate performance (R² = 0.491), and the ANN exhibited good performance (R² = 0.767) for the holdout set. We also evaluated the performance of the MLR and ANN models in predicting O₃ for five months after the calibration period and the results showed moderate correlations (R²s of 0.427 and 0.567, respectively). These low-cost MO sensors combined with a long-term ANN calibration model can complement reference measurements to understand geospatial and temporal differences in O₃ levels.

Chemical conditioning is an effective strategy for improved river sediment dewatering affecting both the dewatering efficiency and subsequent resource utilization of the dewatered cake. Two types of conditioning agents, polyaluminium chloride (PAC)/cationic polyacrylamide (PAM) (coagulation precipitation conditioning agent, referred to as P–P conditioning) and ferrous activated sodium persulfate (advanced oxidation conditioning agent, referred to as F–S conditioning) were examined. With increasing leach liquid to solid (L/S) ratio the concentration of Cd for the real time leachates from the dewatered cakes decreased, but the leaching ratio of Cd in both P–P and F–S dewatered cakes increased. With the same L/S, the leaching ratio was reduced for both types of conditioning, as compared to no conditioning, with the leaching ratio being least with F–S conditioning. The leaching ratio of Cd in the dewatered cake with L/S of 100 L kg⁻¹ was reduced from 21.3% of the total Cd present for the un-conditioned sediment to 12.5% upon P–P conditioning and 11.6% upon F–S conditioning. Furthermore, the different conditioning methods affected the Cd speciation in the dewatered cakes reducing the easy-to-leach speciation of exchangeable and carbonate-bound Cd species and increasing the potential-to-leach speciation of iron-manganese oxide and organically bound Cd species and also the difficult-to-leach species. Risk assessment indicates that the risk due to Cd leaching from the dewatered cakes at L/S of 100 L kg⁻¹ was reduced from high risk to medium risk after P–P and F–S conditioning with reduced bioavailability.

This work presents the results of a PM2.5 source apportionment study conducted in urban background sites from 16 European and Asian countries. For some Eastern Europe and Central Asia cities this was the first time that quantitative information on pollution source contributions to ambient particulate matter (PM) has been performed. More than 2200 filters were sampled and analyzed by X-Ray Fluorescence (XRF), Particle-Induced X-Ray Emission (PIXE), and Inductively Coupled Plasma Mass Spectrometry (ICP-MS) to measure the concentrations of chemical elements in fine particles. Samples were also analyzed for the contents of black carbon, elemental carbon, organic carbon, and water-soluble ions. The Positive Matrix Factorization receptor model (EPA PMF 5.0) was used to characterize similarities and heterogeneities in PM2.5 sources and respective contributions in the cities that the number of collected samples exceeded 75. At the end source apportionment was performed in 11 out of the 16 participating cities. Nine major sources were identified to have contributed to PM2.5: biomass burning, secondary sulfates, traffic, fuel oil combustion, industry, coal combustion, soil, salt and “other sources”. From the averages of sources contributions, considering 11 cities 16% of PM2.5 was attributed to biomass burning, 15% to secondary sulfates, 13% to traffic, 12% to soil, 8.0% to fuel oil combustion, 5.5% to coal combustion, 1.9% to salt, 0.8% to industry emissions, 5.1% to “other sources” and 23% to unaccounted mass. Characteristic seasonal patterns were identified for each PM2.5 source. Biomass burning in all cities, coal combustion in Krakow/POL, and oil combustion in Belgrade/SRB and Banja Luka/BIH increased in Winter due to the impact of domestic heating, whereas in most cities secondary sulfates reached higher levels in Summer as a consequence of the enhanced photochemical activity. During high pollution days the largest sources of fine particles were biomass burning, traffic and secondary sulfates.

Current practices employed by most wastewater treatment plants (WWTP) are unable to completely remove endocrine disrupting compounds (EDCs) from reclaimed waters, and consistently discharge these substances to receiving systems. Effluent-dominated and dependent surface waters, especially during low instream flows, can increase exposure and risks to aquatic organisms due to adverse biological effects associated with EDCs. Given the ecological implications that may arise from exposure to such compounds, the present a multi-approach study examined spatio-temporal estrogenic potential of wastewater effluent to fish in East Canyon Creek (ECC), Utah, USA, a unique urban river with instream flows seasonally influenced by snowmelt. Juvenile rainbow trout (Oncorhynchus mykiss) were caged at different upstream and downstream sites from an effluent discharge during the summer and fall seasons. In the summer, where approximately 50% of the streamflow was dominated by effluent, fish from the upstream and a downstream site, located 13 miles away from the effluent discharge, presented significantly elevated concentrations of plasma vitellogenin (VTG). Similarly, significantly high 17β-estradiol to 11-ketotestosterone ratios were measured in the summer across all sites and time points, compared to the fall. In the laboratory, juvenile fish and primary hepatocytes were exposed to concentrated effluent and surface water samples. Quantification of VTG, although in significantly lower levels, resembled response patterns observed in fish from the field study. Furthermore, analytical quantification of common EDCs in wastewater revealed the presence of estriol and estrone, though these did not appear to be related to the observed biological responses, as these were more significant in sites were no EDCs were detected. These combined observations suggest potential estrogenicity for fish in ECC under continuous exposures and highlight the advantages of following weight-of-evidence (WoE) approaches for environmental monitoring, as targeted analytically-based assessments may or may not support the identification of causative contaminants for adverse biological effects.

Very little is currently known regarding the effects of estrogenic endocrine disrupting chemicals on embryonic and larval development in molluscs, nor the potential effects of parental (F₀) exposure on resultant F₁ offspring. In this study, we assessed the embryotoxic impacts of exposure to environmentally relevant concentrations of the synthetic estrogen, 17α-ethinylestradiol (EE2), to male and female parents (50 ng/L) and their offspring (5 and 50 ng/L) in the native Australian Sydney rock oyster, Saccostrea glomerata. There were no detectable effects of parental exposure on fertilisation success, proportions of early larval (F₁) morphs and unfertilised eggs. Offspring impacts were evidenced in terms of developmental delays, with decreased percentages of D-veligers retained by 45 μm mesh, along with a reduction of swimming capabilities of larvae at 2 days post-fertilisation (dpf) when both parents had been exposed to 50 ng/L EE2. Although no significant parental effects were found on the survival of F₁ larvae at 9 dpf, retardation of shell growth was observed on F₁ larvae in treatments where both parents had been exposed to 50 ng/L EE2. Subsequent larval exposure from 2 to 9 dpf caused declines in survival and reduction of shell length in F₁ larvae at both 5 and 50 ng/L EE2 across all parental exposure treatments. Collectively, parental EE2 imparts effects on offspring in terms of retardation of larval development, and subsequent offspring exposure to EE2 further exacerbates impacts to development. Future research should aim to understand the potential mechanisms of EE2 induced toxicity and its transmission resulting in altered phenotypes of the F₁ generation.

Low pH and aluminum (Al)-toxicity often coexist in acidic soils. Citrus sinensis seedlings were treated with nutrient solution at a pH of 2.5, 3.0, 3.5 or 4.0 and an Al concentration of 0 or 1 mM for 18 weeks. Thereafter, malate, citrate, isocitrate, acid-metabolizing enzymes, and nonstructural carbohydrates in roots and leaves, and release of malate and citrate from roots were measured. Al concentration in roots and leaves increased under Al-toxicity, but it declined with elevating nutrient solution pH. Al-toxicity increased the levels of glucose, fructose, sucrose and total soluble sugars in leaves and roots at each given pH except for a similar sucrose level at pH 2.5–3.0, but it reduced or did not alter the levels of starch and total nonstructural carbohydrates (TNC) in leaves and roots with the exception that Al improved TNC level in roots at pH 4.0. Levels of nonstructural carbohydrates in roots and leaves rose with reducing pH with a few exceptions with or without Al-toxicity. A potential model for the possible role of root organic acid (OA) metabolism (anions) in C. sinensis Al-tolerance was proposed. With Al-toxicity, the elevated pH upregulated the OA metabolism, and increased the flow of carbon to OA metabolism, and the accumulation of malate and citrate in roots and subsequent release of them, thus reducing root and leaf Al and hence eliminating Al-toxicity. Without Al-toxicity, low pH stimulated the exudation of malate and citrate, an adaptive response of Citrus to low pH. The interactive effects of pH and pH on OA metabolism were different between roots and leaves.

Narrow city streets surrounded by tall buildings are favorable to inducing a general effect of a “canyon” in which pollutants strongly accumulate in a relatively small area because of weak or inexistent ventilation. In this study, levels of nitrogen-oxide (NO₂), elemental carbon (EC) and organic carbon (OC) mass concentrations in PM₁₀ particles were determined to compare between seasons and different years. Daily samples were collected at one such street canyon location in the center of Zagreb in 2011, 2012 and 2013. By applying machine learning methods we showed seasonal and yearly variations of mass concentrations for carbon species in PM₁₀ and NO₂, as well as their covariations and relationships. Furthermore, we compared the predictive capabilities of five regressors (Lasso, Random Forest, AdaBoost, Support Vector Machine and Partials Least squares) with Lasso regression being the overall best performing algorithm. By showing the feature importance for each model, we revealed true predictors per target. These measurements and application of machine learning of pollutants were done for the first time at a street canyon site in the city of Zagreb, Croatia.

Environmental complexity leads to differences in the spatial distribution of heavy metal pollution in soil and rice. Such spatial differences will seriously affect the safety of planted rice and can impact regional management and control. How to scientifically reveal these spatial differences is an urgent problem. In this study, the spatial mismatch relationship between Cd pollution in soil and rice grains (brown rice) was first explored by the interpolation method. To further reveal the causes of these, the specific recognition rules of the spatial relationship of Cd pollution were extracted based on a decision tree model, and the results were mapped. The results revealed a spatial mismatch in Cd pollution between the soil and rice grains in the study area, and the main results are as follows: (i) slight soil pollution and safe rice accounted for 68.88% of the area; (ii) slight soil pollution and serious rice pollution accounted for 13.39% of the area and (iii) safe soil and serious rice pollution accounted for 11.63% of the area. In addition, 11 recognition rules of Cd spatial pollution relationship between soil and rice were proposed, and the main environmental factors were determined: SOM (soil organic matter), Dis-residence (distance from residential area), soil pH and LAI (leaf area index). The average accuracy of rule recognition was 75.90%. The study reveals the spatial mismatch of heavy metal pollution in soil and crops, providing decision-making references for the spatial accurate identification and targeted prevention of heavy metal pollution spaces.

Fine particulate matter (PM₂.₅) pollution has become a worldwide environmental concern because of its adverse impacts on human health. This study aimed to explore the spatiotemporal variations and influencing factors of PM₂.₅ concentrations in Beijing during the 2013–2018 period, and further analyzed the impacts of environmental protection policies implemented in recent years. Notably, this study employed various statistical methods, i.e., ordinary Kriging interpolation, spatial autocorrelation analysis, time-series analysis and the Bonferroni test, to evaluate the regional and seasonal differences of PM₂.₅ concentrations based on long-term monitoring data. The results illustrated that PM₂.₅ concentrations decreased on a yearly basis, demonstrating that air pollution control policies have achieved initial success. Furthermore, PM₂.₅ concentrations were higher in the winter and in the southern regions. Diurnal variation presented a bimodal distribution, which varied slightly with the season. Relative humidity and wind speed were the principal meteorological factors affecting the distribution of PM₂.₅ concentrations, while precipitation had essentially no effect. A high positive correlation between PM₂.₅ and gaseous pollutants (SO₂, NO₂, and CO) indirectly reflected the contribution of automobile exhaust and coal-fired emissions. Generally, PM₂.₅ concentrations demonstrated strong spatiotemporal variations, and meteorological factors and pollutant emissions played an important role in this.

The hydro-acoustic noise radiating from underwater tunnels during vehicle passage may be harmful to aquatic fauna, and this is a particular concern for endangered species. Therefore, the effects of underwater noise radiation and propagation on aquatic biodiversity must be investigated. In this study, the dynamic response of the sediment and tunnel structure in the Yangtze River in China was explored by conducting a field test, and the associated noise radiation from the tunnel was recorded and investigated. A three-dimensional numerical model was then developed to simulate the vibration of the tunnel-sediment coupling system induced by random traffic-flow models. Next, a modal acoustic transfer vector-based method was used to predict underwater noise radiation by use of a three-dimensional finite-element acoustic model. Finally, the accuracy of the simulated results was verified by comparison with measurements. The results showed that the noise radiation induced by passing vehicles was approximately 14 dB greater than the background noise, with a main frequency range of 12–25 Hz. The random traffic-flow model had obvious influence of the simulated noise level above 20 Hz. Vehicle-induced underwater noise may thus have a direct effect on fish species that can perceive low-frequency sound pressure. The proposed method can be used for further investigation of methods to reduce the effect of underwater noise on aquatic fauna, especially endangered species.