Coastal harmful algal blooms (HABs) in China’s seas have attracted researchers’ attention for decades. Among the four seas of China, the HAB frequency is the highest in the East China Sea (ECS). The impact of climate change and anthropogenic dominant factors on HABs is not well quantified and the response of HABs to the changing climate is also not clear. Here, we compiled a time series of observation-based HAB events since the 1980s and performed a regional assessment to elucidate the dominant drivers of HAB events in the ECS. The results showed that the increase in the frequency of HAB events in the ECS between 2000 and 2003 was associated with increases in dissolved inorganic phosphorus and sea surface temperature anomalies as well as decreasing summer precipitation. The declining annual frequency in HAB events in the ECS after 2003 was associated with the two climatological factors, most notably, precipitation. Under the “business-as-usual” scenario, climate change will increase the annual HAB events in the ECS from the historical frequency (1985–2013) by more than five-fold by the end of 21st century. These findings demonstrated that management strategies based on reducing nutrient loading also need to consider the effects of climate change in the future.

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.

The influence of North African (NAF) dust events on the air quality at the regional level (12 representative monitoring stations) in Southern Europe during a long time series (2007–2014) was studied. PM10 levels and chemical composition were separated by Atlantic (ATL) and NAF air masses. An increase in the average PM10 concentrations was observed on sampling days with NAF dust influence (42 μg m⁻³) when compared to ATL air masses (29 μg m⁻³). Major compounds such as crustal components and secondary inorganic compounds (SIC), as well as toxic trace elements derived from industrial emissions, also showed higher concentrations of NAF events. A source contribution analysis using positive matrix factorisation (PMF) 5.0 of the PM10 chemical data, discriminating ATL and NAF air mass origins, allowed the identification of five sources: crustal, sea salt, traffic, regional, and industrial. A higher contribution (74%) of the natural sources to PM10 concentrations was confirmed under NAF episodes compared with ATL. Furthermore, there was an increase in anthropogenic sources during these events (51%), indicating the important influence of the NAF air masses on these sources. The results of this study highlight that environmental managers should take appropriate actions to reduce local emissions during NAF events to ensure good air quality.

Exposure to methylmercury (MeHg) through fish is a global public health problem. Exposure monitoring is essential for health risk assessment, especially in pregnant women and children due to the documented neurotoxicity. Herein, we evaluate a time series of MeHg exposure via fish in primiparous Swedish women, covering a time period of 23 years (1996–2019). The 655 included mothers were part of the POPUP study (Persistent Organic Pollutants in Uppsala Primiparas) conducted by the Swedish Food Agency (SFA). MeHg exposure was assessed via measurements of total mercury (Hg) in hair using either cold vapor atomic fluorescence spectrophotometry or inductively coupled plasma mass spectrometry, showing very good linear agreement (R² = 0.97). Maternal characteristics and fish consumption were obtained via questionnaires. The median concentration of total Hg in hair was 0.38 mg/kg (range 0.17–1.5) in 1996 and 0.25 mg/kg (range 0.03–1.1) in 2019. On average the women consumed 11 ± 8.2 meals of fish per month, and fish consumption was positively correlated with total Hg in hair (Spearman correlation: 0.39; p < 0.001). In multiple regression analyses, the geometric mean annual decrease of total Hg in hair was −2.5% (95% CI: -3.2, −1.8%). Total fish consumption increased up to 2011 (B: 0.32 times/month per year; 95% CI 0.17, 0.46) after which it started to decline (B: -0.66 times/month per year; 95% CI -0.92, −0.40). Moreover, both total Hg in hair and fish consumption was positively associated with maternal age and education, and inversely associated with pre-pregnancy BMI. In conclusion, the exposure to MeHg via fish appears to be slowly declining among Swedish pregnant women.

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.

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.

Schizophrenia is a devastating neuropsychiatric disorder with increasing concern. Limited studies have been conducted to assess the relationship between short-term exposure to ambient air pollution and schizophrenia attacks. This study aimed to investigate the associations between short-term air pollution exposure and schizophrenia outpatient visits based on a time-series study performed in China. Daily data of schizophrenia outpatient admissions and air pollution from 1 October 2010 to 31 December 2013 were collected in Xi'an, a heavily-polluted city in China. We utilized a time-series Poisson regression model to examine the associations between short-term air pollution and schizophrenia outpatient visits with different lag days. A total of 34,865 outpatient-visits for schizophrenia were identified. A 10 μg/m³ increase of PM₁₀, SO₂, and NO₂ concentrations corresponded to 0.289% (95% Cl: 0.118%, 0.460%), 1.374% (95% Cl: 0.723%, 2.025%), and 1.881% (95% Cl: 0.957%, 2.805%) elevation in outpatient-visits for schizophrenia at lag 0, and the associations appeared to be stronger, although not statistically significantly, in females and in middle and older age adults (40 and over). The most significant associations were observed on the concurrent day in different lag models. In conclusion, short-term exposure to ambient air pollution (PM₁₀, SO₂, and NO₂) can be associated with increased risk of daily outpatient visits for schizophrenia, which may contribute to the further understanding of the potential adverse effects of air pollution in schizophrenia and other neuropsychiatric disorders.