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.

Particulate matter from wood burning emissions (Cwₒₒd) was quantified at five locations in the United Kingdom (UK), comprising three rural and two urban sites between 2009 and 2021. The aethalometer method was used. Mean winter Cwₒₒd concentrations ranged from 0.26 μg m⁻³ (in rural Scotland) to 1.30 μg m⁻³ (London), which represented on average 4% (in rural environments) and 5% (urban) of PM₁₀ concentrations; and 8% of PM₂.₅. Concentrations were greatest in the evenings in winter months, with larger evening concentrations in the weekends at the urban sites. Random-forest (RF) machine learning regression models were used to reconstruct Cwₒₒd concentrations using both meteorological and temporal explanatory variables at each site. The partial dependency plots indicated that temperature and wind speed were the meteorological variables explaining the greatest variability in Cwₒₒd, with larger concentrations during cold and calm conditions. Peaks of Cwₒₒd concentrations took place during and after events that are celebrated with bonfires. These were Guy Fawkes events in the urban areas and on New Year's Day at the rural sites; the later probably related to long-range transport. Time series were built using the RF. Having removed weather influences, long-term trends of Cwₒₒd were estimated using the Theil Sen method. Trends for 2015–2021 were downward at three of the locations (London, Glasgow and rural Scotland), with rates ranging from −5.5% year⁻¹ to −2.5% year⁻¹. The replacement of old fireplaces with lower emission wood stoves might explain the decrease in Cwₒₒd especially at the urban sites The two rural sites in England observed positive trends for the same period but this was not statistically significant.

Numerous epidemiological studies have shown a close relationship between outdoor air pollution and increased risks for cancer, infection, and cardiopulmonary diseases. However, very few studies have investigated the potential health effects of coexposure to airborne particulate matter (PM) and bioaerosols through the transmission of infectious agents, particularly under the current circumstances of the coronavirus disease 2019 pandemic. In this study, we aimed to identify urinary metabolite biomarkers that might serve as clinically predictive or diagnostic standards for relevant diseases in a real-time manner. We performed an unbiased gas/liquid chromatography-mass spectroscopy (GC/LC-MS) approach to detect urinary metabolites in 92 samples from young healthy individuals collected at three different time points after exposure to clean air, polluted ambient, or purified air, as well as two additional time points after air repollution or repurification. Subsequently, we compared the metabolomic profiles between the two time points using an integrated analysis, along with Kyoto Encyclopedia of Genes and Genomes-enriched pathway and time-series analysis. We identified 33 and 155 differential metabolites (DMs) associated with PM and bioaerosol exposure using GC/LC-MS and follow-up analyses, respectively. Our findings suggest that 16-dehydroprogesterone and 4-hydroxyphenylethanol in urine samples may serve as potential biomarkers to predict or diagnose PM- or bioaerosol-related diseases, respectively. The results indicated apparent differences between PM- and bioaerosol-associated DMs at five different time points and revealed dynamic alterations in the urinary metabolic profiles of young healthy humans with cyclic exposure to clean and polluted air environments. Our findings will help in investigating the detrimental health effects of short-term coexposure to airborne PM and bioaerosols in a real-time manner and improve clinically predictive or diagnostic strategies for preventing air pollution-related diseases.

To determine whether natural recovery was occurring in a depositional area of the St. Marys River (Ontario, Canada) known as East Bellevue Marine Park (EBMP), sediment was collected from two depth ranges, 0–5 cm and 0–10 cm, and subjected to a series of laboratory toxicity tests and chemical analysis. Toxicological responses (survival, growth, reproduction, development) of four benthic invertebrates and the fathead minnow were compared at test vs. reference sites using univariate and multivariate (ordination) techniques. Temporal trends in sediment chemistry and invertebrate toxicity were examined with time series data from 2008 through to 2018. Polycyclic aromatic hydrocarbons (PAHs; ≤ 37 mg/kg) and petroleum hydrocarbons (PHCs; ≤ 6266 mg/kg) were elevated in EBMP compared to reference sites (PAHs, ≤ 1.6 mg/kg; PHCs ≤ 180 mg/kg). Comparatively, the 0–5 cm sediment layer had lower concentrations of all contaminants than the 0–10 cm layer at three of four test sites. Over time, contaminant concentrations have mostly remained stable or have decreased. There were no significant differences in survival, growth, or development of the larval fish in EBMP compared to the upstream reference sites, and no differences between sampling depths. However, most EBMP sediments were toxic to invertebrates, driven by reduced reproduction by the worm Tubifex and reduced survival by the amphipod Hyalella. Among habitat variables, a combination of different classes of compounds based on ordination scores (PHCs, oil and grease, metals) was most strongly correlated to toxicological response. There was little to no difference in toxicity between sampling depths based on integrated endpoint response; however, individual endpoints showed mostly greater toxicity from exposure to the 0–10 cm layer. Over time, toxicity has mostly remained stable or showed improvement. These results provided some positive indications that gradual natural recovery is occurring in EBMP.

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.

Air quality forecasting for Hong Kong is a challenge. Even taking the advantages of auto-regressive integrated moving average and some state-of-the-art numerical models, a recently-developed hybrid method for one-day (two- and three-day) ahead forecasting performs similarly to (slightly better than) a simple persistence forecasting. Long-term forecasting also remains an important issue, especially for policy decision for better control of air pollution and for evaluation of the long-term impacts on public health. Given the well-recognized negative effects of PM₂.₅, NO₂ and O₃ on public health, we study their time series under the multi-scale framework with empirical mode decomposition and nonstationary oscillation resampling to explore the possibility of long-term forecasting and to improve short-term forecasts in Hong Kong. Applied to a dataset from January 2016 to December 2018, the long-term forecasting (with lead time about 100 days) of the multi-scale framework has the root-mean-square error (RMSE) comparable with that of the short-term (with lead time of one or two days) forecasting by the persistence method, while its improvement for short-term forecasting (with lead time of one, two or three days) is quite substantial over the persistence forecasting, with RMSEs reduced by respectively 44%–47%, 30%–45%, and 40%–60% for PM₂.₅, NO₂, and O₃. Compared to the hybrid method, it turns out that, for short-term forecasting for the same data, the multi-scale framework can reduce RMSE by about 25% (respectively 30%) for PM₂.₅ (respectively NO₂ and O₃). In addition, we find no significant difference in the forecasting performance of the multi-scale framework among different types of stations. The multi-scale framework is feasible for time series forecasting and applicable to other pollutants in other cities.

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.

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.

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.