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.

The molecular mechanism of evaluating 17β-estradiol (E₂)-induced toxicity in female Daphnia magna has not been determined. In this study, the transcriptome of D. magna was analyzed after exposure to three different concentrations (0, 10, and 100 ng L⁻¹) of E₂ at 3, 6, and 12 h. The results showed 351–17,221 significantly up-regulated and 505–10,282 significantly down-regulated genes (P < 0.05). Overall, the selected largest 10,282 (10 ng L⁻¹vs control at 12 h) down-regulated and 17,221 (100 vs 10 ng L⁻¹) up-regulated genes were identified; following annotation, pathways in cancer and RNA transport were found to be enriched according to the interaction network. Among all completed comparisons, KEGG pathways related to the immune system, cancer, disease infection, and active compound metabolism were identified by short time series expression miner analysis. A different set of genes fluctuated in a “U”-shaped pattern over time and at different concentrations of E₂, whereas some genes associated with disintoxication showed a reverse “U”-shaped response as E₂ administration was increased. These results suggest that E₂ exposure caused transcriptional changes in the immune system, disintoxication, disease prevention, and the protein degradation pathway.

Ammonia (NH₃) emissions could have significant impacts on both ecosystems and human health. Ice cores from the Tibetan Plateau contain information about past ammonium (NH₄⁺) deposition, which could yield important insights into historical NH₃ emissions in the surrounding source regions as well as long-distance NH₄⁺ aerosol transport via atmospheric circulation. In this paper, we present a high-resolution atmospheric NH₄⁺ deposition record for the period, 1951–2008, reconstructed from the Zangser Kangri (ZK) ice core in the northern Tibetan Plateau. An empirical orthogonal function (EOF) analysis of major soluble ions (NH₄⁺, NO₃⁻, SO₄²⁻, Cl⁻, Na⁺, K⁺, Mg²⁺ and Ca²⁺) reveals that EOF 1 has significant loadings of all ions, therefore representing common transport pathways, while EOF 2 is only significantly loaded by NH₄⁺ (0.86) and NO₃⁻ (0.35), suggesting a unique signal possibly representing emissions from the surrounding terrestrial ecosystems on the Tibetan Plateau. Backward trajectory analysis indicates that the air masses over the ZK ice core drilling site primarily come from the northwestern Indian Peninsula. NH₃ emissions from agricultural activities in this area likely contribute to the NH₄⁺ deposition of the ZK ice core via the Indian monsoon. Correlations between EOF 2 time series and temperature, normalized difference vegetation index (NDVI) suggest that increasing temperature and vegetation after 1980 likely promoted NH₃ emissions from terrestrial ecosystems. Our results provide a reliable and valuable assessment of NH₄⁺ deposition from human activities and terrestrial ecosystems in the ZK ice core, and help in understanding air pollution over the past few decades in the northern Tibetan Plateau.

Fine particulate matter (PM₂.₅) pollution has become a worldwide environmental concern because of its adverse impacts on human health. This study aimed to explore the spatiotemporal variations and influencing factors of PM₂.₅ concentrations in Beijing during the 2013–2018 period, and further analyzed the impacts of environmental protection policies implemented in recent years. Notably, this study employed various statistical methods, i.e., ordinary Kriging interpolation, spatial autocorrelation analysis, time-series analysis and the Bonferroni test, to evaluate the regional and seasonal differences of PM₂.₅ concentrations based on long-term monitoring data. The results illustrated that PM₂.₅ concentrations decreased on a yearly basis, demonstrating that air pollution control policies have achieved initial success. Furthermore, PM₂.₅ concentrations were higher in the winter and in the southern regions. Diurnal variation presented a bimodal distribution, which varied slightly with the season. Relative humidity and wind speed were the principal meteorological factors affecting the distribution of PM₂.₅ concentrations, while precipitation had essentially no effect. A high positive correlation between PM₂.₅ and gaseous pollutants (SO₂, NO₂, and CO) indirectly reflected the contribution of automobile exhaust and coal-fired emissions. Generally, PM₂.₅ concentrations demonstrated strong spatiotemporal variations, and meteorological factors and pollutant emissions played an important role in this.

Spatiotemporal variations in PM₂.₅ are a key factor affecting personal pollution exposure levels in urban areas. However, fixed-site monitoring stations are so sparsely distributed that they hardly capture the dynamic and fine-scale variations in PM₂.₅ in urban areas with complex geographical features and urban forms. Recently, a distributed air sensor network (DASN) was deployed in Dezhou city, China, to monitor fine-scale air pollution information and obtain deep insight into variations in PM₂.₅. Based on the data collected by the DASN, this paper investigated the spatiotemporal patterns of PM₂.₅ using the time-series clustering method. The results demonstrated that there were four stages of PM₂.₅ daily variations, i.e., accumulation, continuous pollution, dispersion, and cleaning. Generally, the stage of dispersion occurred more rapidly than the stage of accumulation, and PM₂.₅ accumulated easily in warm and humid weather with low wind speeds. However, the stage of dispersion was affected mainly by high wind speeds and precipitation. Additionally, the results suggested that four variation stages did not strictly correspond to seasonal divisions. The spatial distributions of PM₂.₅ revealed that the main pollution source was located in a southeastern industrial park, which exhibited a significant impact throughout the four stages. Considering both the temporal and spatial characteristics of PM₂.₅, this study successfully identified pollution hotspots and confirmed the effect of industrial parks. The study demonstrates that the DASN has high prospective applicability for assessing the fine-scale spatial distribution of PM₂.₅, and the time-series clustering method can also assist environmental researchers in further exploring the spatiotemporal characteristics of urban air pollution.

Contamination of diverse environments and wild species by some contaminants is projected to continue and increase in coming decades. In the marine environment, large volumes of data to assess how concentrations have changed over time can be gathered from indicator species such as seabirds, including through sampling feathers from archival collections and museums. As apex predators, Flesh-footed Shearwaters (Ardenna carneipes) are subject to high concentrations of bioaccumulative and biomagnifying contaminants, and reflect the health of their local marine environment. We analysed Flesh-footed Shearwater feathers from Australia from museum specimens and live birds collected between 1900 and 2011 and assessed temporal trends in three trace elements of toxicological concern: cadmium, mercury, and lead. Concentrations of cadmium increased by 1.5% per year (95% CI: +0.6, +3.0), while mercury was unchanged through the time series (−0.3% per year; 05% CI: -2.1, +1.5), and lead decreased markedly (−2.1% per year, 95% CI: -3.2, −1.0). A reduction in birds’ trophic position through the 20th century, and decreased atmospheric emissions were the likely driving factors for mercury and lead, respectively. By combining archival material from museum specimens with contemporary samples, we have been able to further elucidate the potential threats posed to these apex predators by metal contamination.

Groundwater pollution early warning has been regarded as an effective tool for regional groundwater pollution prevention, especially in China. In this study, the systemic model was established to assess the groundwater pollution early warning by integrating the present situation of groundwater quality (Q), groundwater quality trend (T) and groundwater pollution risk (R). The model integrated spatial and temporal variation of groundwater quality, and combined the state and process of the groundwater pollution. Q, T and R were assessed by the methods of fuzzy comprehensive assessment, Spearman or nonparametric Mann-Kendall trend test, and overlay index, respectively. Taking the Luoyang City as an example, the groundwater pollution early warning mapping was generated, and verified by corresponding the groundwater quality classes and the early warning degrees. The results showed that the groundwater was dominated by the levels of no warning and light warning, which accounted for 77% of the study area. The serious and tremendous warning areas were affected by the worse trend and relatively bad/bad present situations of groundwater quality with the typical contaminants of total hardness, nitrate, Hg and COD. In summary, the present situation of groundwater quality was the most important factor of groundwater pollution early warning mapping in the study area. The worse trend of groundwater quality played equally a key role in the local regions, as well as the high pollution risk, which was mainly affected by the pollution source loading. Targeted measures for groundwater pollution prevention were proposed in the corresponding degrees of groundwater pollution early warning. The QTR model was proved to be effective for assessing the regional groundwater pollution early warning. The accuracy of the model could be improved if there is further data acquisition of groundwater quality in longer time series and in larger number, and further investigation of pollution sources.The QTR model is proposed and proved to be effective for assessing regional groundwater pollution early warning.

This study explores the generation of ultrafine particle emissions, measured in particle number (PN), based on a portable emissions measurement system (PEMS) in the City of Toronto between October and December 2019. Two driving routes were designed to include busy arterial roads and highways. All measurements were conducted between 10 a.m. and 4 p.m. Altogether, emissions from 31 drives were collected, leading to approximately 200,000 s of data. A spike detection algorithm was employed to isolate PN spikes in time series data. A sensitivity analysis was also conducted to identify the most optimum method for spike detection. The results indicate that the average emission rate during a PN spike is approximately 8 times the emission rate along the rest of the drive. In each test trip, about 25% of the duration was attributed to spike events, contributing 75% of total PN emissions. A Pearson correlation of 0.45 was estimated between the number of PN spikes and the number of sharp accelerations (above 8.5 km/h/s). The Pearson correlation between the occurrence of high engine torque (above 65.0 Nm) and the number of PN spikes was estimated at 0.80. The number of PN spikes was highest on arterial roads where the vehicle speed was relatively low, but with high variability, and including a high number of sharp accelerations. This pattern of UFP emissions leads to high UFP concentrations along arterial roads in the inner city core.

Clinical or pathological evidence demonstrated that air pollution could undermine other organ systems of human body besides respiratory and circulation systems. Investigations that directly relate hospital outpatient visits for endocrine (ENDO), digestive (DIGE), urological (UROL), and dermatological (DERM) diseases categories with ambient particulate matter (PM) are still lacking, particularly in heavily polluted cities. Here, we conducted a time-series analysis using 812,624, 1,111,342, 539,803, and 741,662 hospital visits for ENDO, DIGE, UROL, and DERM, respectively, in Nanjing, China from 2013 to 2019. A generalized additive model was applied to estimate the exposure-response associations. Results showed that a 10 μg/m³ increase in PM₂.₅ concentration on lag 0 day was significantly associated with 0.59% (95% CI: 0.30%, 0.88%), 0.43% (0.15%, 0.70%), 0.36% (0.06%, 0.66%), and 0.65% (0.42%, 0.87%) increase for ENDO, DIGE, UROL, and DERM hospital visits, respectively. The estimated effects of PM₁₀ were slightly smaller but still statistically significant. The magnitude and significance of the associations between PM and four health outcomes were sensitive to additional adjustment for co-pollutants. Exposure-response relationships were linear for PM concentrations lower than 100 μg/m³ but the curves became nonlinear across the full range of exposures due to a flatten slope at higher concentrations. We also explored the effect modifications by season (cold or warm), age (5–18, 18–64, 65–74, or 75+ years), and sex (male or female). Results showed that the DERM-related population aged 65 years or older was more vulnerable to PM exposure, compared with the 5 to 17-year age group; the DERM-related population aged 75 years or older and 65 years or older was more vulnerable to PM₂.₅ and PM₁₀ exposure, respectively, compared with the 18 to 64-year age group. Our study provided suggestive evidence that ambient PM pollution was associated with ENDO, DIGE, UROL, and DERM outpatient hospital visits in Nanjing, China.

High levels of air pollutants in Vietnam, especially particulate matters including PM₂.₅, can be important risk factors for respiratory diseases among children of the country. However, few studies on the effects of ambient air pollution on human health have been conducted in Vietnam so far. The aim of this study is to examine the association between PM₂.₅ and hospital admission due to acute lower respiratory infection (ALRI) among children aged < 5 years old in Ho Chi Minh city, the largest city of Vietnam. Data relating PM₂.₅ and hospital admission were collected from February 2016–December 2017 and a time series regression analysis was performed to examine the relationship between PM₂.₅ and hospital admission including the delayed effect up to three days prior to the admission. We found that each 10 μg/m³ increase in PM₂.₅ was associated with an increase of 3.51 (95%CI: 0.96–6.12) risk of ALRI admission among children. According to the analysis, male children are more sensitive to exposure to PM₂.₅ than females, while children exposed to PM₂.₅ are more likely to be infected with acute bronchiolitis than with pneumonia. The study demonstrated that young children in HCMC are at increased risk of ALRI admissions due to the high level of PM₂.₅ concentration in the city's ambient air.