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.

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.

Although dust storms have been associated with adverse health outcomes, studies on the burden of dust storms on deaths are limited. As global warming has induced significant climate changes in recent decades, which have accelerated desertification worldwide, it is necessary to evaluate the burden of dust storm-induced premature mortality using a critical measure of disease burden, such as the years of life lost (YLL). The YLL attributable to dust storms have not been examined to date. This study investigated the association between Asian dust storms (ADS) and the YLL in Seoul, South Korea, during 2002–2013. We conducted a time-series study using a generalized additive model assuming a Gaussian distribution and applied a distributed lag model with a maximum lag of 5 days to investigate the delayed and cumulative effects of ADS on the YLL. We also conducted stratified analyses using the cause of death (respiratory and cardiovascular diseases) and sociodemographic status (sex, age, education level, occupation, and marital status). During the study period, 108 ADS events occurred, and the average daily YLL was 1511 years due to non-accidental causes. The cumulative ADS exposure over the 6-day lag period was associated with a significant increase of 104.7 (95% CI, 31.0–178.5 years) and 34.4 years (4.0–64.7 years) in the YLL due to non-accidental causes and cardiovascular mortality, respectively. Sociodemographic analyses revealed associations between ADS exposure and the YLL in males, both <65 and ≥ 65 years old, those with middle-level education, and the unemployed, unmarried, and widowed (26.5–83.8 years). This study provides new evidence suggesting that exposure to dust storms significantly increases the YLL. Our findings suggest that dust storms are a critical environmental risk affecting premature mortality. These results could contribute to the establishment of public health policies aimed at managing dust storm exposure and reducing premature deaths.

Soil contamination by potentially toxic elements (PTEs) is one of the greatest threats to environmental degradation. Knowing where PTEs accumulated in soil can mitigate their adverse effects on plants, animals, and human health. We evaluated the potential of using long-term remote sensing images that reveal the bare soils, to detect and map PTEs in agricultural fields. In this study, 360 soil samples were collected at the superficial layer (0–20 cm) in a 2574 km² agricultural area located in São Paulo State, Brazil. We tested the Soil Synthetic Image (SYSI) using Landsat TM/ETM/ETM+, Landsat OLI, and Sentinel 2 images. The three products have different spectral, temporal, and spatial resolutions. The time series multispectral images were used to reveal areas with bare soil and their spectra were used as predictors of soil chromium, iron, nickel, and zinc contents. We observed a strong linear relationship (−0.26 > r > −0.62) between the selected PTEs and the near infrared (NIR) and shortwave infrared (SWIR) bands of Sentinel (ensemble of 4 years of data), Landsat TM (35 years data), and Landsat OLI (4 years data). The clearest discrimination of soil PTEs was obtained from SYSI using a long term Landsat 5 collection over 35 years. Satellite data could efficiently detect the contents of PTEs in soils due to their relation with soil attributes and parent materials. Therefore, distinct satellite sensors could map the PTEs on tropics and assist in understanding their spatial dynamics and environmental effects.

Industrial parks emit large amounts of anthropogenic heat and aggravate the urban heat island effect, which has become a severe environmental problem worldwide. Few studies explored if the warming effect generated by concentrated industrial facilities (i.e., steel plants in this study) produces an intra-heat island effect in urban built-up areas. Sufficient evidence of an industrial heat island (IHI) effect is lacking, and new quantitative methods are urgently needed to address these issues. Therefore, we proposed a new scheme to quantify the warming effect of large, heat-emitting urban objects versus complex surroundings, and the IHI effect was accordingly defined at a finer scale. This study separated the industrial park from other artificial lands and comprehensively estimated the IHI effects' spatiotemporal variation. The IHI intensities were measured based on varied natural and urbanized references, which provided new evidence for the existence of the IHI effect over space and seasons. The land surface temperature (LST) profiles delineated the downward trend in LST variation from inside to surroundings in the IHI cases on both spatial and temporal scales. The time-series analysis revealed that the IHI effects demonstrated more significant disparities regarding the LSTs between the industrial parks and their surrounding backgrounds during warm seasons than in cold seasons. And a more severe IHI effect was observed in spring and summer, and the weakest IHI intensity occurred in winter. Moreover, the IHI intensity is positively associated to the anthropogenic heat, indicating that the industrial activities contribute to the increased LSTs of the industrial park to a great extent. The rationale of the IHI effect can broaden insight for understanding how urban industrial heat sources influence the regional thermal environment, especially at a finer scale.

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.

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.

Poor air quality is an emerging problem in Australia primarily due to ozone pollution events and lengthening and more severe wildfire seasons. A significant deterioration in air quality was experienced in Australia’s most populous cities, Melbourne and Sydney, as a result of fires during the so-called Black Summer which ran from November 2019 through to February 2020. Following this period, social, mobility and economic restrictions to curb the spread of the COVID-19 pandemic were implemented in Australia. We quantify the air quality impact of these contrasting periods in the south-eastern states of Victoria and New South Wales (NSW) using a meteorological normalisation approach. A Random Forest (RF) machine learning algorithm was used to compute baseline time series’ of nitrogen dioxide (NO₂), ozone (O₃), carbon monoxide CO and particulate matter with diameter < 2.5 μm (PM₂.₅), based on a 19 year, detrended training dataset. Across Victorian sites, large increases in CO (188%), PM₂.₅ (322%) and ozone (22%) were observed over the RF prediction in January 2020. In NSW, smaller pollutant increases above the RF prediction were seen (CO 58%, PM₂.₅ 80%, ozone 19%). This can be partly explained by the RF predictions being high compared to the mean of previous months, due to high temperatures and strong wind speeds, highlighting the importance of meteorological normalisation in attributing pollution changes to specific events. From the daily observation-RF prediction differences we estimated 249.8 (95% CI: 156.6–343.) excess deaths and 3490.0 (95% CI 1325.9–5653.5) additional hospitalisations were likely as a result of PM₂.₅ and O₃ exposure in Victoria and NSW. During April 2019, when COVID-19 restrictions were in place, on average NO₂ decreased by 21.5 and 8% in Victoria and NSW respectively. O₃ and PM₂.₅ remained effectively unchanged in Victoria on average but increased by 20 and 24% in NSW respectively, supporting the suggestion that community mobility reduced more in Victoria than NSW. Overall the air quality change during the COVID-19 lockdown had a negligible impact on the calculated health outcomes.

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.

Particulate air pollution is a continuing challenge in China, and its adverse effects on chronic obstructive pulmonary disease (COPD) have been widely reported. However, epidemiological evidence on the associations between size-fractionated particle number concentrations (PNCs) and COPD mortality is limited. In this study, we utilized a time-series approach to investigate the associations between PNCs of particles at 0.25–10 μm in diameter and COPD mortality in Shanghai, China. Quasi-Poisson regression generalized additive models were applied to evaluate these associations, with adjustment of time trend, day of week, holidays, temperature and relative humidity. Stratification analyses were performed by season and gender. There were a total of 3238 deaths due to COPD during the study period. We found that daily COPD deaths were significantly associated with PNCs of particles <0.5 μm, and the magnitude of associations increased with decreasing particle size. An interquartile range (IQR) increase in PNC₀.₂₅—₀.₂₈, PNC₀.₂₈—₀.₃, PNC₀.₃—₀.₃₅, PNC₀.₃₅—₀.₄, PNC₀.₄—₀.₄₅ and PNC₀.₄₅—₋₀.₅ was associated with increments of 7.51% (95%CI: 2.45%, 12.81%), 7.22% (95%CI: 2.16%, 12.53%), 6.95% (95%CI: 1.81%, 12.35%), 6.26% (95%CI: 1.25%, 11.52%), 5.24% (95%CI: 0.56%, 10.13%) and 4.15% (95%CI: 0.14%, 8.32%), respectively. The associations remained robustness after controlling for the mass concentrations of gaseous air pollutants. In stratification analyses, significant associations between PNCs and COPD mortality were observed in the cold seasons, and in males. Our results suggested that particles <0.5 μm in diameter might be most responsible for the adverse effects of particulate air pollution on COPD mortality, and COPD patients are more susceptible to PM air pollution in the cold seasons, especially for males.