Long-term change and shorter-term variability in the atmospheric deposition of pollutants and marine salts can have major effects on the biogeochemistry and ecology of soils and surface water ecosystems. In the 1980s, at the time of peak acid deposition in the UK, deposition loads were highly dependent on prevailing weather types, and it was postulated that future pollution recovery trajectories would be partly dependent on any climate change-driven shifts in weather systems. Following three decades of substantial acidic emission reductions, we used monitoring data collected between 1992 and 2015 from four UK Environmental Change Network (ECN) sites in contrasting parts of Great Britain to examine the trends in precipitation chemistry in relation to prevailing weather conditions. Weather systems were classified on the basis of Lamb weather type (LWT) groupings, while emissions inventories and clustering of air mass trajectories were used to interpret the observed patterns. Concentrations of ions showed clear differences between cyclonic-westerly-dominated periods and others, reflecting higher marine and lower anthropogenic contributions in Atlantic air masses. Westerlies were associated with higher rainfall, higher sea salt concentrations, and lower pollutant concentrations at all sites, while air mass paths exerted additional controls. Westerlies therefore have continued to favour higher sea salt fluxes, whereas emission reductions are increasingly leading to positive correlations between westerlies and pollutant fluxes. Our results also suggest a shift from the influence of anthropogenic emissions to natural emissions (e.g., sea salt) and climate forcing as they are transported under relatively cleaner conditions to the UK. Westerlies have been relatively frequent over the ECN monitoring period, but longer-term cyclicity in these weather types suggests that current contributions to precipitation may not be sustained over coming years.

Hexachloronaphthalene (HxCN) is one of the most toxic and most bioaccumulative congeners of polychlorinated naphthalenes (PCNs) known to be present in animal and human adipose tissue. Unfortunately, little data is available regarding the negative effect of PCNs on endocrine function. The aim of the study was to investigate the direct influence of subacute (two and four-week) and subchronic (13-week) daily oral exposure of female rats to 30, 100 and 300 μg kg b.w.⁻¹ HxCN on ovarian, thyroid function and neurotransmitters level. The levels of selected sex hormones (progesterone: P and estradiol: E2) in the serum and uterus, regularity of estrous cycle, levels of thyroid hormones (fT3 and fT4), TSH, γ-aminobutyric acid and glutamate levels in selected brain areas and the activity of CYP1A1 and CYP2B in the liver were examined. Estrogenic action (elevated E2 concentration in the uterus and serum) was observed only after subacute exposure, and antiestrogenic activity (decreased E2 level and uterus weight) after 13 weeks administration of 300 μg kg b.w.⁻¹ day⁻¹. Subchronic administration of HxCN significantly lengthens the estrous cycle, by up to almost 50%, and increases the number of irregular cycles. In addition, increased TSH and decreased fT4 serum levels were observed after all doses and durations of exposure to HxCN. Only subacute exposure led to a significant decrease in the level of examined neurotransmitters in all analyzed structures. Additionally, exposure to low doses of HxCN appears to lead to strong induction of CYP1A1 in a liver. It can be hypothesized that HxCN produces effects which are very similar to those caused by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) and dioxin-like compounds (DLCs), particularly concerning endocrine and estrous cyclicity disorders. Therefore, HxCN exposure may exert unexpected effects on female fecundity among the general population.

4-nitrophenol (PNP), isolated from diesel exhaust particles, has estrogenic and anti-androgenic activities, and affects the hypothalamus-pituitary-gonad axis in male rats. However, the effect of PNP on the reproduction of the female rats is still unknown. The aim of the study was to investigate the effect of neonatal PNP exposure on the ovarian function of female rats. The neonatal female rats were exposed to PNP (10 mg/kg, subcutaneously injection), the ovary and serum samples were collected at postnatal day (PND) 7, 14 and 21. The results showed that the ratio of primordial and primary follicles increased whereas the ratio of antral follicles decreased in the PNP treated ovaries at PND21. Even though no abnormality was observed in cyclicity, there was a significantly delayed timing of vaginal opening in PNP treated rats. The ovarian expressions of steroidogenic enzymes including StAR, P450scc, P450c17 and P450arom increased at PND14 in the PNP treated rats compared with the control rats. In consistent with the gene expressions, the concentration of estradiol-17β showed the similar pattern. However, PNP exposure failed to cause any significant change in the expressions of steroidogenic enzymes in cultured neonatal ovaries. Furthermore, PNP suppressed the expression of estrogen receptor β (ERβ), but not estrogen receptor α (ERα), in cultured ovaries or developmental ovaries. These results suggested that PNP might directly affect the expression of ERβ in the rat ovaries, resulting in the disrupted steroidogenesis during ovarian development and the delayed puberty.

Over the last decades, changes in dust storms characteristics have been observed in different parts of the world. The changing frequency of dust storms in the southeastern Mediterranean has led to growing concern regarding atmospheric PM10 levels. A classic time series additive model was used in order to describe and evaluate the changes in PM10 concentrations during dust storm days in different cities in Israel, which is located at the margins of the global dust belt. The analysis revealed variations in the number of dust events and PM10 concentrations during 2001–2015. A significant increase in PM10 concentrations was identified since 2009 in the arid city of Beer Sheva, southern Israel. Average PM10 concentrations during dust days before 2009 were 406, 312, and 364 μg m−3 (median 337, 269,302) for Beer Sheva, Rehovot (central Israel) and Modi'in (eastern Israel), respectively. After 2009 the average concentrations in these cities during dust storms were 536, 466, and 428 μg m−3 (median 382, 335, 338), respectively. Regression analysis revealed associations between PM10 variations and seasonality, wind speed, as well as relative humidity. The trends and periodicity are stronger in the southern part of Israel, where higher PM10 concentrations are found. Since 2009 dust events became more extreme with much higher daily and hourly levels. The findings demonstrate that in the arid area variations of dust storms can be quantified easier through PM10 levels over a relatively short time scale of several years.

French Polynesia atolls are spread on a vast 2300 by 1200 km Central Pacific Ocean area exposed to spatially and temporally dependent wave forcing. They also have a wide range of closed to open morphologies and several have been suitable to develop from black-lipped pearl oysters a substantial pearl farming activity in the past 30 years, representing nowadays the 2nd source of income for French Polynesia. Considering here only the component of lagoon renewal that is driven by waves, we investigate for 74 atolls different lagoon renewal metrics using 20 years of wave model data at 0.05° spatial resolution. Wavelet spectral analyses highlight that atolls, even in close vicinity, can be exposed to different and characteristic periodicities in wave-driven flows and water renewal. These characteristics are discussed in relation to pearl farming atolls, including atolls known to be efficient oyster spat producers, a critical activity for pearl farming sustainability.

Large areas of hypoxia have been reported off The Changjiang Estuary and in the East China Sea. Five cruises, covering winter, spring, and summer, were carried out from 2007 to 2013 in this region, and in August 2013 (summer), an extensive hypoxic event (11,150km2) was observed, which was characterized by an estimated bulk oxygen depletion of 5.1 million tons. A strong tidal impact was observed associated with the bottom oxygen depletion, with the periodicity of diel variations in dissolved oxygen being 12h (i.e., similar to the tidal cycle). A conservative estimate of nutrient regeneration suggested that during the hypoxic event of August 2013, the amount of regenerated nitrogen (as nitrate) and phosphorus (as dissolved inorganic phosphorus) was 27,000–30,000 tons and 1300–41,000tons, respectively. Estimates of the absolute (bulk) regenerated nutrient fluxes were much greater than the conservative estimates.

We developed generalised additive mixed models (GAMMs) to infer spatiotemporal trends of environmental PCB concentrations from an extensive dataset (n=1219) of PCB concentrations measured between 1991 and 2010 in sediments of the Belgian Coastal Zone (BCZ) and the Western Scheldt estuary. A GAMM with time, geographical zone, periodicity and the organic carbon – water partition coefficient as covariates explained 49% of the variability in the log transformed PCB sediment concentrations. The time trends unraveled two to threefold PCB concentration decreases in the BCZ during the last 20years. However, in the Western Scheldt estuary, time trends were spatially heterogeneous and not significantly decreasing. These results demonstrate that international efforts to cut down emissions of PCBs have been effective to reduce concentrations in open water ecosystems like the BCZ but had little effect in the urbanised and industrialised area of the Scheldt estuary.

Combined thermal power (CHP) production mode plays a more important role in energy production, but the impact of its pollutant emission on the natural environment is still difficult to eradicate. Traditional pollutant control adopts post-treatment process to degrade the generated pollutants, but there is little research on controlling the generation of pollutants from the source. Therefore, starting from the source, this paper predicts the pollutants through the prediction model, so as to provide countermeasures for production regulation and avoiding excessive emission. In this paper, a pollution emission prediction method of CHP systems based on feature engineering and a hybrid deep learning model is proposed. Feature engineering performs multi-step preprocessing on the original data, refines the correlation factors, and removes redundant variables. The hybrid deep learning model has a multi-variable input and is established by combining the convolutional neural network, long short-term memory network with the attention mechanism. The case study is conducted on the collected actual dataset. The influence of the prediction target periodicity on the prediction results is analyzed seasonally to verify the effectiveness of the hybrid model. The results show that the root mean square error of the proposed method is less than one, and the error is reduced compared to the other basic methods, which proves the superiority of the proposed pollution emission prediction method over the existing methods.

The perception of spatio-temporal variability of drought is important in concerning the food security of a country. The native aim of this study is to extract the spatio-temporal variability of drought over India with implications on agriculture. We have opted for SPI-3 as the primary index for drought quantification. The spatio-temporal variability of SPI-3 is evaluated through empirical orthogonal functional (EOF) analysis to extract the prominent patterns of drought variability over the study region. The first two dominant patterns of SPI-3 explain (38%) the total variability and are mainly influenced by global teleconnections. The EOF patterns while subjected to spectrum analysis depict that the first mode shows 7.7 years of cycle and the second mode shows 2.6 years of the cycle. On seeing the interference of El Nino Southern Oscillation (ENSO) on drought, we found that drought years are mainly influenced by ENSO with the same periodicity (2–7 years/cycle) as that of EOF patterns. The dynamics of drought show that the persistence of high pressure along East and West Asia during drought years has declined the monsoon activity over India leading to a shortfall of rainfall in monsoon months. On the other hand, we have found that the drought years have drawn implications on agricultural production by stifling the total annual production of most of the drought years. This research would have a wide range of applications in forecasting extreme events in India, allowing for better preparation and management of the water resource system during droughts.

Machines learning models have recently been proposed for predicting rivers water temperature (Tw) using only air temperature (Tₐ). The proposed models relied on a nonlinear relationship between the Tw and Tₐ and they have proven to be robust modelling tools. The main motivation for this study was to evaluate how the variational mode decomposition (VMD) contributed to the improvement of machines learning performances for river Tw modelling. Measured data collected at five stations located in Poland from 1987 to 2014 were acquired and used for the analysis. Six machines learning models were used and compared namely, K-nearest neighbor’s regression (KNNR), least square support vector machine (LSSVM), generalized regression neural network (GRNN), cascade correlation artificial neural networks (CCNN), relevance vector machine (RVM), and locally weighted polynomials regression (LWPR). The six models were developed according to three scenarios. First, the models were calibrated using only the Tₐ as input and obtained results show that the models were able to predict consistently water temperature, showing a high determination coefficient (R²) and Nash–Sutcliffe efficiency (NSE) with values near or above 0.910 and 0.915, respectively, and in overall the six models worked equally without clear superiority of one above another. Second, the air temperature was combined with the periodicity (i.e., day, month and year number) as input variable and a significant improvement was achieved. Both models show their ability to accurately predict river Tw with an overall accuracy of 0.956 for R² and 0.955 for NSE values, but the LSSVM2 have some advantages such as a small errors metrics, and high fitting capabilities and it slightly surpasses the others models. Thirdly, air temperature was decomposed into several intrinsic mode functions (IMF) using the VMD method and the performances of the models were evaluated. The VMD parameters appeared to cause much influence on the prediction accuracy, exhibiting an improvement of about 40.50% and 39.12% in terms of RMSE and MAE between the first and the third scenarios, however, some models, i.e., GRNN and KNNR have not benefited from the VMD. This research has demonstrated the high capability of the VMD algorithm as a preprocessing approach in improving the accuracies of the machine learning models for river water temperature prediction.