In this study, a multitude of statistical tools were used to examine PM10 concentration trends and their seasonal behavior from 2015 to 2021 in Tehran. The results of the integrated analysis have led to a better understanding of current PM10 trends which may be useful for future management policies. The Kruskal – Wallis test indicated the significant impact of atmospheric phenomena on the seasonal fluctuations of PM10. The seasonal decomposition of PM10 time series was conducted for better analysis of trends and seasonal oscillations. The seasonal Mann-Kendall test illustrated the significant possibility of a monotonic seasonal trend of PM10 (p = 0.026) while showing its negative slope simultaneously (Sen = -1.496). The forecasting procedure of PM10 until 2024 comprised 15 time series models which were validated by means of 8 statistical criteria. The model validation results indicated that ARIMA (0,1,2) was the most satisfactory case for predicting the future trend of PM10. This model estimated the concentration of PM10 to reach approximately 79.04 (µg/m3) by the end of 2023 with a 95% confidence interval of 51.38 – 107.42 (µg/m3). Overall, it was concluded that the use of the aforementioned analytical tools may help decision-makers gain a better insight into future forecasts of ambient airborne particulate matter.

The problematic of microplastics pollution in the marine environment is tightly linked to their colonization by a wide diversity of microorganisms, the so-called plastisphere. The composition of the plastisphere relies on a complex combination of multiple factors including the surrounding environment, the time of incubation along with the polymer type, making it difficult to understand how the biofilm evolves during the microplastic lifetime over the oceans. To better define bacterial community assembly processes on plastics, we performed a 5 months spatio-temporal survey of the plastisphere in an oyster farming area in the Bay of Brest (France). We deployed three types of plastic pellets in two positions in the foreshore and in the water column. Plastic-associated biofilm composition in all these conditions was monitored using 16 S rRNA metabarcoding and compared to free-living and attached bacterial members of seawater. We observed that bacterial families associated to plastic pellets were significantly distinct from the ones found in seawater, with a significant prevalence of filamentous Cyanobacteria on plastics. No convergence towards a unique plastisphere was detected between polymers exposed in the intertidal and subtidal area, emphasizing the central role of the surrounding environment on constantly shaping the plastisphere community diversity. However, we could define a bulk of early-colonizers of marine biofilms such as Alteromonas, Pseudoalteromonas or Vibrio. These early-colonizers could reach high abundances in floating microplastics collected in field-sampling studies, suggesting the plastic-associated biofilms could remain at early development stages across large oceanic scales. Our study raises the hypothesis that most members of the plastisphere, including putative pathogens, could result of opportunistic colonization processes and unlikely long-term transport.

While the effects of ambient pollutants on adverse perinatal outcomes have been studied, most studies have focused on preterm birth, stillbirth, and low birthweight. Few studies have examined the effects of ambient pollutants on prelabor rupture of membranes (PROM). This study was designed to explore the acute effects of ambient pollutants on both term PROM (TPROM) and preterm PROM (PPROM). We enrolled pregnant women receiving antenatal care between October 2013 and December 2019 at the International Peace Maternity and Child Health Hospital (IPMCHH). The effects of ambient pollutants (including PM₂.₅, PM₁₀, SO₂, CO, NO₂, and 8-h O₃) on TPROM and PPROM were estimated using generalized additive models (GAMs). Exposure-response relationship curves were also evaluated using GAMs after adjustment for confounding factors. Potential lagged effects were examined using various lag models. The data of 100,200 pregnant women who delivered at IPMCHH were analyzed. The fitted spline curves for PPROM were similar to the temporal trends of PM₂.₅, PM₁₀, SO₂, CO and NO₂ but not O₃, while those for TPROM were different from the temporal trends of all six air pollutants. An increased risk of PPROM was associated with increased concentrations of PM₂.₅, PM₁₀, SO₂ and CO on lag days 2 and 3, while no association was found between PPROM and daily concentration of O₃. After adjustment for confounding factors, there was a shift in the exposure-response curves, indicating associations between PPROM and PM₂.₅, PM₁₀, SO₂, and CO on lag days 2–3. Interaction effects of PM₂.₅, PM₁₀, SO₂, and CO were also found to increase the risk of PPROM. In conclusion, acute exposures to six critical air pollutants were not associated with an increased risk of TPROM; however, PM₂.₅, PM₁₀, SO₂, and CO were found to interact, increasing the risk for PPROM on lag days 2 and 3.

With increasingly stringent regulations on emission criteria and environment pollution concerns, marine fuel oils (particularly heavy fuel oils) that are commonly used today for powering ships will no longer be allowed in the future. Various maritime energy strategies are now needed for the long-term upgrade that might span decades, and quantitative predictions are necessary to assess the outcomes of their implementation for decision support purpose. To address the technical need, a novel approach is developed in this study that can incorporate the strategic implementation of fuel choices and quantify their adequacy in meeting future environmental pollution legislations for ship emissions. The core algorithm in this approach is based on probabilistic simulations with a large sample size of ship movement in the designated port area, derived using a Bayesian ship traffic generator from existing real activity data. Its usefulness with scenario modelling is demonstrated with application examples at five major ports, namely the Ports of Shanghai, Singapore, Tokyo, Long Beach, and Hamburg, for assessment at Years 2020, 2030, and 2050 with three economic scenarios. The included fuel choices in the application examples are comprehensive, including heavy fuel oils, distillates, low sulphur fuel oils, ultra-low sulphur fuel oils, liquefied natural gas, hydrogen, biofuel, methanol, and electricity (battery). Various features are fine-tuned to reflect micro-level changes on the fuel choices, terminal location, and/or ship technology. Future atmospheric pollutant emissions with various maritime energy strategies implemented at these ports are then discussed comprehensively in details to demonstrate the usefulness of the approach.

On March 12th, 2020, the WHO declared COVID-19 as a pandemic. The collective impact of environmental and ecosystem factors, as well as biodiversity, on the spread of COVID-19 and its mortality evolution remain empirically unknown, particularly in regions with a wide ecosystem range. The aim of our study is to assess how those factors impact on the COVID-19 spread and mortality by country. This study compiled a global database merging WHO daily case reports with other publicly available measures from January 21st to May 18th, 2020. We applied spatio-temporal models to identify the influence of biodiversity, temperature, and precipitation and fitted generalized linear mixed models to identify the effects of environmental variables. Additionally, we used count time series to characterize the association between COVID-19 spread and air quality factors. All analyses were adjusted by social demographic, country-income level, and government policy intervention confounders, among 160 countries, globally. Our results reveal a statistically meaningful association between COVID-19 infection and several factors of interest at country and city levels such as the national biodiversity index, air quality, and pollutants elements (PM₁₀, PM₂.₅, and O₃). Particularly, there is a significant relationship of loss of biodiversity, high level of air pollutants, and diminished air quality with COVID-19 infection spread and mortality. Our findings provide an empirical foundation for future studies on the relationship between air quality variables, a country’s biodiversity, and COVID-19 transmission and mortality. The relationships measured in this study can be valuable when governments plan environmental and health policies, as alternative strategy to respond to new COVID-19 outbreaks and prevent future crises.

Silver nanomaterials (AgNMs) are broadly used and among the most studied nanomaterials. The underlying molecular mechanisms (e.g. protein and metabolite response) that precede phenotypical effects have been assessed to a much lesser extent. In this paper, we assess differentially expressed proteins (DEPs) and metabolites (DEMs) by high-throughput (HTP) techniques (HPLC-MS/MS with tandem mass tags, reversed-phase (RP) and hydrophilic interaction liquid chromatography (HILIC) with mass spectrometric detection). In a time series (0, 7, 14 days), the standard soil model Enchytraeus crypticus was exposed to AgNM300K and AgNO₃ at the reproduction EC20 and EC50. The impact on proteins/metabolites was clearly larger after 14 days. NM300K caused more upregulated DEPs/DEMs, more so at the EC20, whereas AgNO₃ caused a dose response increase of DEPs/DEMs. Similar pathways were activated, although often via opposite regulation (up vs down) of DEPs, hence, dissimilar mechanisms underlie the apical observed impact. Affected pathways included e.g. energy and lipid metabolism and oxidative stress. Uniquely affected by AgNO₃ was catalase, malate dehydrogenase and ATP-citrate synthase, and heat shock proteins (HSP70) and ferritin were affected by AgNM300K. The gene expression-based data in Adverse Outcome Pathway was confirmed and additional key events added, e.g. regulation of catalase and heat shock proteins were confirmed to be included. Finally, we observed (as we have seen before) that lower concentration of the NM caused higher biological impact. Data was deposited to ProteomeXchange, identifier PXD024444.

Air pollution is an important risk factor for autoimmune diseases, but its association with the recurrence of rheumatoid arthritis (RA) remains unclear so far. This study aimed to investigate the short-term association between traffic-related air pollutants and hospital readmissions for RA in Hefei, China. Data on daily hospital readmissions for RA and traffic-related air pollutants, including particulate matter (PM₂.₅ and PM₁₀), nitrogen dioxide (NO₂), and carbon monoxide (CO), from 2014 to 2018 were retrieved. A time-series approach using generalized linear regression model was employed. The analysis was further stratified by sex, age and season. A total of 1153 readmissions for RA were reported during the study period. A significant association between high-concentration PM₂.₅ (90th percentile) and RA readmissions was observed on lag1 (relative risk (RR) = 1.09, 95% confidence interval (CI): 1.01–1.19) and lasted until lag3 (RR = 1.06, 95%CI: 1.01–1.12). From lag2 to lag5, high-concentration NO₂ (90th percentile) was associated with increased risk of RA readmissions, with the highest RR observed at lag 4 (1.11, 95%CI: 1.05–1.17). Stratified analyses indicated that females and the elderly appeared to be more vulnerable to high-concentration PM₂.₅ and NO₂ exposure. High-concentration PM₂.₅ and NO₂ in cold seasons were consistently significantly associated with increased risk of RA readmissions. Exposure to high-concentration PM₂.₅ and NO₂ was associated with increased risk of RA readmissions. Protective measures against the exposure to high-concentration PM₂.₅ and NO₂ should be taken to reduce the recurrence risk in RA patients, especially in females, the elderly and during cold seasons.

Groundwater chemistry can be affected by and related to earthquakes, thus it is crucial to understand the hydrochemical changes and associated processes caused by earthquakes for post-seismic groundwater utilization. Here we reported the major ion concentrations changes of the Ganze Spring in response to the May 30, 2014 Ms 6.1 Yingjiang earthquake, southwest China based on the daily time series (from 1st January 2012 to 20th July 2014) of Ca²⁺, Mg²⁺ and HCO₃⁻ concentrations, as well as data of bulk strain and Peak Ground Velocity (PGV) recorded at a nearby station. The results showed that the entire hydrochemical response process can be divided into two stages after the earthquake occurred: 1). decline stage which was characterized by an increasingly decline of the three ion concentrations, indicating a gradually significant dilution effect. At first, the relationship of molar concentrations of ions showed no obvious changes; but later as the rate of decrease in ion concentrations increased, the relationship between Ca²⁺ and HCO₃⁻ reversed from Ca²⁺ excess to HCO₃⁻ excess, probably resulting from a relatively decreased Ca²⁺ contribution from dissolution of gypsum and dolomite due to dilution in mixing water. 2). recover stage when the ion concentrations recovered gradually with relatively lower values than that at pre-earthquake, revealing the reduction of dilute water inflow. In combination with the bulk strain and PGV data, the study suggested that major ion concentrations changes are attributed to dilution effect due to new fracture creation or unclogging/clogging of fractures triggered by the earthquake. The results could enhance the understanding of earthquake induced water chemistry changes and could have implications for water resources management and security in tectonically active areas.

Organic fraction municipal solid waste (OFMSW) has a high potential for energy and value-added product recovery due to its carbon- and nutrient-rich composition; however, traditional value chains have treated OFMSW as an undesired by-product. This study focuses on value chain optimisation to assist the transition to resource recovery value chains. To achieve this, this work combined two stage stochastic mathematical optimisation with geographical spatial analysis and time series waste generation analysis. Existing infrastructure in England, including anaerobic digestion plants and road transportation networks, were included in the model. To account for uncertainty in waste generation, multiple scenarios and their associated probabilities were developed based on environmental variables. The optimisation problem was solved to further advance the understanding of economically optimal waste-to-resource value chains under waste generation variability. The pertinent decision variables included sizing, technology selection, waste flows and location of thermochemical treatment sites. The model highlights the potential reduction in system profitability as a result of different operating constraints, such as minimum plant operating capacity factors and landfill taxation. The latter was shown to have the largest impact on profitability as overconservative systems designs were implemented to hedge against the waste variability. Such computer-aided models offer opportunities to overcome the challenges posed by waste generation variability and waste to resource value chain transformation.

Identification of pollution point source in rivers is strenuous due to accidental chemical spills or unmanaged wastewater discharges. It is crucial to take physical characteristics into account in the estimation of pollution sources. In this study, an integrated inverse modeling framework is developed to identify a point source of accidental water pollution based on the contaminant concentrations observed at monitoring sites in time series. The modeling approach includes a Markov chain Monte Carlo method based on Bayesian inference (Bayesian-MCMC) inverse model and a genetic algorithm (GA) inverse model. Both inverse models can estimate the pollution sources, including the emission mass quantity, release time, and release position in an accidental river pollution event. The developed model is first tested for a hypothetical case with field river conditions. The results show that the source parameters identified by the Bayesian-MCMC inverse model are very close to the true values with relative errors of 0.02% or less; the GA inverse model also works with relative errors in the range of 2%–7%. Additionally, the uncertainties associated with model parameters are analyzed based on global sensitive analysis (GSA) in this study. It is also found that the emission mass of pollution source positively correlates with the dispersion coefficient and the river cross-sectional area, whereas the flow velocity significantly affects release position and release time. A real case study in the Fen River is further conducted to test the applicability of the developed inverse modeling approach. Results confirm that the Bayesian-MCMC model performs better than the GA model in terms of accuracy and stability for the field application. The findings of this study would support decision-making during emergency responses to river pollution incidents.