While considerable efforts have been made to address the relationship between urbanization and environmental issues, few of them focus on household emissions. Thus, this work aimed at evaluating the effect of urbanization on household wastewater emissions, and made a distinction between the efficient- and the inefficient-emissions. We compiled a China’s provincial dataset over the period 2005–2017, and estimates indicated that a 1% rise in the urbanization was correlated with a 0.581 increase of efficient emissions, while inefficient emissions decreased by 7.606. As of 2017, the sample period end year, the inefficient emissions accounted for 10.5% of the total emissions of China as a whole, which was relatively large and could not be overlooked. Meanwhile, a back-of-the-envelope estimate revealed that urbanization had a negative impact on China’s total emissions, with a marginal effect ranging from −0.226 to −1.354 over the sample period. The above findings, however, did not imply that urbanization would spontaneously reduce the inefficient- and total-emissions. Thus, the government in the process of urbanization should strengthen environmental education, municipal facilities, and others.

Accurate estimation of historical PM2.5 exposures for epidemiological studies is challenging when extensive monitoring data are limited in duration. Here, we develop a national-scale PM2.5 exposure model for Japan using measurements recorded between 2014 and 2016 to estimate monthly means for 1987 through 2016. Our objective is to obtain accurate PM2.5 estimates for years prior to implementation of extensive PM2.5 monitoring, using observations from a limited period. We utilize a neural network to convey the non-linear relationship between the target pollutant and predictors, while incorporating the associated air pollutants. We obtain high R² values of 0.76 and 0.73 through spatial and temporal cross validation, respectively. We evaluate estimation accuracy using an independent data set and achieve an R² of 0.75. Moreover, monthly variations for 2000–2013 are well reproduced with correlation coefficients of greater than 0.78, obtained through a comparison with observations. We estimate monthly means at 1 × 1 km resolution from 1987 through 2016. The estimates show decreases in the area and population weighted means beginning in the 1990s. We successfully estimate monthly mean PM2.5 concentrations over three decades with outstanding predictive accuracy. Our findings illustrate that the presented approach achieves accurate long-term historical estimations using observations limited in duration.

Endometriosis is a gynaecological disease characterised by the presence of endometriotic tissue outside of the uterus impacting a significant fraction of women of childbearing age. Evidence from epidemiological studies suggests a relationship between risk of endometriosis and exposure to some organochlorine persistent organic pollutants (POPs). However, these chemicals are numerous and occur in complex and highly correlated mixtures, and to date, most studies have not accounted for this simultaneous exposure. Linear and logistic regression models are constrained to adjusting for multiple exposures when variables are highly intercorrelated, resulting in unstable coefficients and arbitrary findings. Advanced machine learning models, of emerging use in epidemiology, today appear as a promising option to address these limitations. In this study, different machine learning techniques were compared on a dataset from a case-control study conducted in France to explore associations between mixtures of POPs and deep endometriosis. The battery of models encompassed regularised logistic regression, artificial neural network, support vector machine, adaptive boosting, and partial least-squares discriminant analysis with some additional sparsity constraints. These techniques were applied to identify the biomarkers of internal exposure in adipose tissue most associated with endometriosis and to compare model classification performance. The five tested models revealed a consistent selection of most associated POPs with deep endometriosis, including octachlorodibenzofuran, cis-heptachlor epoxide, polychlorinated biphenyl 77 or trans-nonachlor, among others. The high classification performance of all five models confirmed that machine learning may be a promising complementary approach in modelling highly correlated exposure biomarkers and their associations with health outcomes. Regularised logistic regression provided a good compromise between the interpretability of traditional statistical approaches and the classification capacity of machine learning approaches. Applying a battery of complementary algorithms may be a strategic approach to decipher complex exposome-health associations when the underlying structure is unknown.

Evidence is accumulating about the impacts of plastics on marine life. The prevalence of plastics in seabird nests has been used as an indicator of levels of this pollutant in the ocean. However, the lack of a framework for defining sample sizes and errors associated with estimating the prevalence of plastic in nests prevents researchers from optimising time and reducing impacts of fieldwork. We present a method to determine the confidence intervals for the prevalence of debris in seabird nests and provide, for the first time, information on the prevalence of these items in nests of the Hartlaub’s gull Larus hartlaubii, the African penguin Spheniscus demersus, the great white pelican Pelecanus onocrotalus, and the white-breasted cormorant Phalacrocorax lucidus in South Africa. The method, based on observations and resampling simulations and tested here for nests of 12 seabird species from 15 locations worldwide, allows for straightforward hypothesis testing. Appropriate sample sizes can be defined by combining this method with a Bayesian approach. We show that precise estimates of prevalence of debris in nests can be obtained by sampling around 250 nests. Smaller sample sizes can be useful for obtaining rough estimates. For the Hartlaub’s gull, the African penguin, the great white pelican, and the white-breasted cormorant, debris were present in 0.75%, 3.00%, 6.41%, and 25.62% of the respective nests. Our approach will help researchers to determine errors associated with the prevalence of debris recorded in seabird nests and to optimise time and costs spent collecting data. It can also be applied to estimate confidence intervals and define sample sizes for assessing prevalence of plastic ingestion by any organism.

There is growing evidence that the very presence of human beings in an enclosed environment can impact air quality by affecting the concentrations of certain airborne volatile organic compounds (VOC). This influence increases considerably when humans perform different activities, such as using toiletries, or simply eating and drinking. To understand the influence of these parameters on the concentrations of selected airborne constituents, a study was performed under simulated residential conditions in an environmentally-controlled exposure room. The human subjects either simply remained for a certain time in the exposure room, or performed pre-defined activities in the room (drinking wine, doing sport, using toiletries, and preparation of a meal containing melted cheese). The impact of each activity was assessed separately using our analytical platform and exposure room under controlled environmental conditions. The results showed that prolonged human presence leads to increased levels of isoprene, TVOCs, formaldehyde and, to a lesser extent, acetaldehyde. These outcomes were further supported by results of meta-analyses of data acquired during several internal studies performed over two years. Furthermore, it was seen that the indoor concentrations of several of the selected constituents rose when the recreational and daily living activities were performed. Indeed, an increase in acetaldehyde was observed for all tested conditions, and these higher indoor levels were especially notable during wine-drinking as well as cheese meal preparation. Formaldehyde increased during the sessions involving sport, using toiletries, and cheese meal preparation. Like acetaldehyde, acrolein, crotonaldehyde and particulate matter levels rose significantly during the cheese meal preparation session. In conclusion, prolonged human residence indoors and some recreational and daily living activities caused substantial emissions of several airborne pollutants under ventilation typical for residential environments.

Field survey-based ecological risk assessments for trace metals are conducted to examine the necessity and/or effectiveness of management intervention, such as setting of environmental quality standards. Observational datasets often involve confounders that may bias estimation of the effects of intervention (e.g., reduction of trace-metal concentrations through regulation). The field of ecotoxicology lags behind some other research fields in understanding proper analytical procedures for causal inference from observational datasets; there are only a few field survey-based ecotoxicological studies that have explicitly controlled for confounders in their statistical analyses. In the present study, we estimated the effect of intervention in nickel concentrations on Ephemeroptera, Plecoptera, and Trichoptera richness in rivers in Japan. We also provide detailed explanations for the backgrounds of spurious associations derived from confounders and on proper analytical procedures for obtaining an unbiased estimate of the targeted intervention effect by using regression analysis. We constructed a multiple regression model based on a causal diagram for aquatic insects and environmental factors, and on “the backdoor criterion,” that enabled us to determine the set of covariates required to obtain an unbiased estimate of the targeted intervention effect from regression coefficients. We found that management intervention in nickel concentrations may be ineffective compared to intervention in organic pollution, and that analysis ignoring the confounders overestimated the effect of intervention in nickel concentrations. Our results highlight the fact that confounders can lead to misjudging the necessity for management of anthropogenic chemical substances. Confounders should be explicitly specified and statistically controlled to achieve a comprehensive assessment of ecological risks for various substances.

Oil spills can result in changes in chemical contaminant concentrations along coastlines. When concentrations are measured along the Gulf of Mexico over time, this information can be used to evaluate oil spill shoreline exposure dates. The objective of this research was to identify more accurate oil exposure dates based on oil spill chemical concentrations changes (CCC) within sediments in coastal zones after oil spills. The results could be used to help improve oil transport models and to improve estimates of oil landings within the nearshore. The CCC method was based on separating the target coastal zone into segments and then documenting the timing of large increases in concentration for specific oil spill chemicals (OSCs) within each segment. The dataset from the Deepwater Horizon (DWH) oil spill was used to illustrate the application of the method. Some differences in exposure dates were observed between the CCC method and between oil spill trajectories. Differences may have been caused by mixing at the freshwater and sea water interface, nearshore circulation features, and the possible influence of submerged oil that is unaccounted for by oil spill trajectories. Overall, this research highlights the benefit of using an integrated approach to confirm the timing of shoreline exposure.

The increasing availability of water quality datasets has led to a greater focus on hydrologic and water quality analysis, thus requiring more efficient and accurate modelling methods. Data mining techniques have been increasingly used for water quality analysis and prediction of the concentration and load of nitrogen pollutants instead of more traditional simulation methods. In this study, we tested the multilayer perceptron (MLP), k-nearest neighbor (k-NN), random forest, and reduced error pruning tree (REPTree) methods, along with the traditional linear regression, to predict nitrate levels based on long-term data from six watersheds with different land-use practices in the midwestern United States. Both the concentration and load results indicated that REPTree had the best performance, with an R² of 0.61–0.85 and a relative absolute error of <75.8%. The different watershed types, however, influenced the performance of the data mining methods, where all four methods showed a higher accuracy for urban dominant watershed and lower accuracy for agricultural and forest watersheds. Out of these four methods, classification tree methods (REPTree and RF) performed better than cluster methods (MLP and k-NN) for agricultural and forested watersheds. Our results indicated that both the data structure based on the dominant land use and type of algorithmic method should be carefully considered for selecting a data mining method to predict nitrate concentration and load for a watershed.

The purpose of this long-term experiment was on gaining more insights into the environmental behaviour of veterinary antibiotics in the subsurface after application with manure. Therefore, manure spiked with a bromide tracer and eight antibiotics (enrofloxacin, lincomycin, sulfadiazine, sulfamethazine, tetracycline, tiamulin, tilmicosin and tylosin) in concentrations of milligrams per litre were applied at an experimental field site. Their pathway was tracked by continuous extraction of soil pore water at different depths and systematic sampling of groundwater for a period of two years. Seven target compounds were detected in soil pore water of which four leached into groundwater. Concentrations of the detected target compounds were, with few exceptions, in the range of nanograms per litre. It was concluded that a large fraction of the investigated antibiotics sorbed or degraded already within the first meter of the soil. Further, it was inferred from the data that long and warm dry periods cause attenuation of the target compounds through increased degradation or sorption occurring in the soil. In addition, the comprehensive data-set allowed to estimate a retardation factor between 1.1 and 2.0 for sulfamethazine in a Plaggic Anthrosol soil, and to classify the individual compounds by environmental relevance based on transport behaviour and persistence. According to the distribution of resistant genes in the environment, sulfamethazine was found to be the most mobile and persistent substance.

Inland waters emit large amounts of carbon dioxide (CO₂) to the atmosphere, but emissions from urban lakes are poorly understood. This study investigated seasonal and interannual variations in the partial pressure of CO₂ (pCO₂) and CO₂ flux from Lake Wuli, a small eutrophic urban lake in the heart of the Yangtze River Delta, China, based on a long-term (2000–2015) dataset. The results showed that the annual mean pCO₂ was 1030 ± 281 μatm (mean ± standard deviation) with a mean CO₂ flux of 1.1 ± 0.6 g m⁻² d⁻¹ during 2000–2015, suggesting that compared with other lakes globally, Lake Wuli was a significant source of atmospheric CO₂. Substantial interannual variability was observed, and the annual pCO₂ exhibited a decreasing trend due to improvements in water quality driven by environmental investment. Changes in ammonia nitrogen and total phosphorus concentrations together explained 90% of the observed interannual variability in pCO₂ (R² = 0.90, p < 0.01). The lake was dominated by cyanobacterial blooms and showed nonseasonal variation in pCO₂. This finding was different from those of other eutrophic lakes with seasonal variation in pCO₂, mostly because the uptake of CO₂ by algal-derived primary production was counterbalanced by the production of CO₂ by algal-derived organic carbon decomposition. Our results suggested that anthropogenic activities strongly affect lake CO₂ dynamics and that environmental investments, such as ecological restoration and reducing nutrient discharge, can significantly reduce CO₂ emissions from inland lakes. This study provides valuable information on the reduction in carbon emissions from artificially controlled eutrophic lakes and an assessment of the impact of inland water on the global carbon cycle.