The limited evidence available suggests that the interaction between chemical pollutants and biological particles may intensify respiratory diseases caused by air pollution in urban areas. Unlike air pollutants, which are routinely measured, records of biotic component are scarce. While pollen concentrations are daily surveyed in most cities, data related to airborne bacteria or fungi are not usually available. This work presents the first effort to understand atmospheric pollution integrating both biotic and abiotic agents, trying to identify relationships among the Proteobacteria, Actinobacteria and Ascomycota phyla with palynological, meteorological and air quality variables using all biological historical records available in the Madrid Greater Region. The tools employed involve statistical hypothesis contrast tests such as Kruskal-Wallis and machine learning algorithms. A cluster analysis was performed to analyse which abiotic variables were able to separate the biotic variables into groups. Significant relationships were found for temperature and relative humidity. In addition, the relative abundance of the biological phyla studied was affected by PM₁₀ and O₃ ambient concentration. Preliminary Generalized Additive Models (GAMs) to predict the biotic relative abundances based on these atmospheric variables were developed. The results (r = 0.70) were acceptable taking into account the scarcity of the available data. These models can be used as an indication of the biotic composition when no measurements are available. They are also a good starting point to continue working in the development of more accurate models and to investigate causal relationships.

In this study, we evaluated the effects of geo-climatic parameters and other potential risk factors on the prevalence of chronic toxoplasmosis (CT) in pregnant women. We searched PubMed/MEDLINE, Web of Science, EMBASE, Scopus, and SciELO databases for seroepidemiological studies published between January 1988, and February 2021. We performed meta-analysis and meta-regression by using a random effect model to synthesize data. A total of 360 eligible datasets, including 1,289,605 pregnant women from 94 countries, were included in this study. The highest and lowest prevalence rates were estimated for latitudes of 0–10° (49.4%) and ≥50° (26.8%); and for the longitude of 80–90° (44.2%) and 110–120° (7.8%), respectively. Concerning climatic parameters, the highest and lowest prevalence rates were estimated in regions with the mean relative humidities of >80% (46.6%) and <40% (27.0); annual precipitation between 1000 and 1500 mm (39.2%) and 250–500 mm (26.8%); and mean annual temperature of 20–30 °C (36.5%), and <7 °C (24.9%), respectively. Meta-regression analyses indicated significant increasing trends in prevalence of CT in pregnant women with decrease in geographical latitude (coefficient, = −0.0035), and geographical longitudes (C = −0.0017). While it was positively associated (P < 0.01) with the mean environmental temperature (C = 0.0047), annual precipitation (C = 0.000064), and mean relative humidity (C = 0.002). Our results highlighted various effects of environmental parameters on the prevalence of CT. Therefore, different regions in the world may benefit from different types of interventions, and thus, novel preventive measures in a region should be developed according to local climate, agricultural activities and people culture.

This research apportioned size-resolved particulate matter (PM) contributions in a megacity in northern China based on a full year of measurements of both inorganic and organic markers. Ions, elements, carbon fractions, n-alkanes, polycyclic aromatic hydrocarbons (PAHs), hopanes and steranes in 9 p.m. size fractions were analyzed. High molecular weight PAHs concentrated in fine PM, while most other organic compounds showed two peaks. Both two-way and three-way receptor models were used for source apportionment of PM in different size ranges. The three-way receptor model gave a clearer separation of factors than the two-way model, because it uses a combination of chemical composition and size distributions, so that factors with similar composition but distinct size distributions (like more mature and less mature coal combustion) can be resolved. The three-way model resolved six primary and three secondary factors. Gasoline vehicles and coal and biomass combustion, nitrate and high relative humidity related secondary aerosol, and resuspended dust and diesel vehicles (exhaust and non-exhaust) are the top two contributors to pseudo-ultrafine (<0.43 μm), fine (0.43–2.1 μm) and coarse mode (>2.1 μm) PM, respectively. Mass concentration of PM from coal and biomass combustion, industrial emissions, and diesel vehicle sources showed a bimodal size distribution, but gasoline vehicles and resuspended dust exhibited a peak in the fine and coarse mode, separately. Mass concentration of sulphate, nitrate and secondary organic aerosol exhibited a bimodal distribution and were correlated with temperature, indicating strong photochemical processing and repartitioning. High relative humidity related secondary aerosol was strongly associated with size shifts of PM, NO₃⁻ and SO₄²⁻ from the usual 0.43–0.65 μm to 1.1–2.1 μm. Our results demonstrated the dominance of primary combustion sources in the <0.43 μm particle mass, in contrast to that of secondary aerosol in fine particle mass, and dust in coarse particle mass in the Northern China megacity.

Indoor air quality ((IAQ) in classrooms was associated with the daily exposure of school-age children who are particularly vulnerable to air pollutants exposure, while few data exist to evaluate classroom indoor air quality nationwide in China. The subsample of the CIEHS 2018 study was performed in 66 classrooms of 22 primary schools nationwide in China. Temperature, relative humidity, PM₂.₅, PM₁₀, CO₂, CO, formaldehyde concentrations, bacteria and fungi were detected in all classrooms by using the instruments that meet the specified accuracy. The ratios of indoor to outdoor (I/O) of PM₂.₅ were calculated in each classroom to identify whether the indoor environment the pollutants comes from outdoors. The indoor PM₂.₅, PM₁₀, CO, HCHO, bacteria and fungi GM concentration are 47.40 μg/m³, 72.91 μg/m³, 0.37 mg/m³, 0.02 mg/m³, 347.51 CFU/m³ and 362.76 CFU/m³, respectively. We observed that there were 66.5%, 52.6%, 22.4%, 1.8%, and 9.6% of the classrooms that exceeded the guideline values of PM₂.₅, PM₁₀, CO₂, HCHO, and bacteria, respectively. It should be attention that all of the classroom's PM₂.₅ concentrations in Shijiazhuang and Nanning, PM₁₀ concentrations in Nanning, CO₂ concentration in Lanzhou were exceeded the suggested values. Bacteria contamination in Shijiazhuang's classrooms is also serious. All classroom CO concentrations meet the requirement. The results indicated that classroom indoor PM₂.₅ was significantly positively correlated with indoor PM₁₀ and CO₂, while was negative correlated with temperature, CO, and fungi. Our results suggest that indoor air pollution in classrooms was a severe problem in Chinese primary schools. It is necessary to strengthen ventilation in the classroom to improve indoor air quality. What's more, a healthy learning environment should be created for primary school students.

Organophosphate esters (OPEs) are ubiquitous contaminants in the environment, whereas their atmospheric processes and fate are poorly understood. The present study revealed the spatial heterogeneity and seasonal variations of traditional and novel OPEs in PM₂.₅ (particulate matter with diameters < 2.5 μm) across a megacity (including residential areas and potential source sites) in South China. Potential influencing factors on the contamination levels of OPEs were addressed. The total concentrations of 11 traditional OPEs ranging from 262 to 42,194 pg/m³ (median = 1872 pg/m³) were substantially higher than those of 10 novel OPEs (33.5–3835 pg/m³, median = 318 pg/m³). Significant spatial and temporal variations in the concentrations of most OPEs were observed. The overall district-specific contamination levels in this city showed dependence on the secondary industry sector for non-predominant OPEs and on the tertiary industry for predominant OPEs. The seasonal variations of the OPE concentrations suggest difference in their sources or influence of meteorological conditions. The correlations between the individual OPEs in PM₂.₅ are determined largely by either their applications or physicochemical properties (in particular vapor pressure). The correlations between OPE concentrations and each meteorological factor (temperature, relative humidity, wind speed, and surface solar radiation) were inconsistent (positive and negative). Wind speed had the greatest effect on the OPE levels; While most OPEs bound to PM₂.₅ were not efficiently scavenged by below-cloud rainfall. The results suggest that atmospheric half-life and Henry’s Law Constant of OPEs are also determining factors for the wind speed and rainfall influence, respectively. However, mechanisms underlying the influence of meteorological conditions on atmospheric OPEs still need further research.

The Clean Air Action implemented by the Chinese government in 2013 has greatly improved air quality in the North China Plain (NCP). In this work, we report changes in the chemical components of atmospheric fine particulate matter (PM₂.₅) at four NCP sampling sites from 2012/2013 to 2017 to investigate the impacts and drivers of the Clean Air Action on aerosol chemistry, especially for secondary inorganic aerosols (SIA). During the observation period, the concentrations of PM₂.₅ and its chemical components (especially SIA, organic carbon (OC), and elemental carbon (EC)) and the frequency of polluted days (daily PM₂.₅ concentration ≥ 75 μg m⁻³) in the NCP, declined significantly at all four sites. Asynchronized reduction in SIA components (large decreases in SO₄²⁻ with stable or even increased NO₃⁻ and NH₄⁺) was observed in urban Beijing, revealing a shift of the primary form of SIA, which suggested the fractions of NO₃⁻ increased more rapidly than SO₄²⁻ during PM₂.₅ pollution episodes, especially in 2016 and 2017. In addition, unexpected increases in the sulfur oxidation ratio (SOR) and the nitrogen oxidation ratio (NOR) were observed among sites and across years in the substantially decreased PM₂.₅ levels. They were largely determined by secondary aerosol precursors (i.e. decreased SO₂ and NO₂), photochemical oxidants (e.g. increased O₃), temperature, and relative humidity via gas-phase and heterogeneous reactions. Our results not only highlight the effectiveness of the Action Plan for improving air quality in the NCP, but also suggest an increasing importance of SIA in determining PM₂.₅ concentration and composition.

Road traffic emissions are an increasingly important source of particulate matter in urban and non-road environments, where non-tailpipe emissions can contribute substantially to elevated levels of metals associated with adverse health effects. Thus, better characterization and quantification of traffic-emitted metals is warranted. In this study, real-world emission factors for fine particulate metals were determined from hourly x-ray fluorescence measurements over a three-year period (2015–2018) at an urban roadway and busy highway. Inter-site differences and temporal trends in real-world emission factors for metals were explored. The emission factors at both sites were within the range of past studies, and it was found that Ti, Fe, Cu, and Ba emissions were 2.2–3.0 times higher at the highway site, consistent with the higher proportion of heavy-duty vehicles. Weekday emission factors for some metals were also higher by 2.0–3.5 times relative to Sundays for Mn, Zn, Ca, and Fe, illustrating a dependence on fleet composition and roadway activity. Metal emission factors were also inversely related to relative humidity and precipitation, due to reduced road dust resuspension under wetter conditions. Correlation analysis revealed groups of metals that were co-emitted by different traffic activities and sources. Determining emission factors enabled the isolation of traffic-related metal emissions and also revealed that human exposure to metals in ambient air can vary substantially both temporally and spatially depending on fleet composition and traffic volume.

Nitrous acid (HONO) production from the heterogeneous photochemical reaction of NO₂ on several Chinese soils was performed in a cylindrical reactor at atmospheric pressure. The NO₂ uptake coefficient (γ) and HONO yield (YHONO) on different soils were (0.42–5.16) × 10⁻⁵ and 6.3%–69.6%, respectively. Although the photo-enhanced uptake of NO₂ on different soils was observed, light could either enhance or inhibit the conversion efficiency of NO₂ to HONO, depending on the properties of the soils. Soils with lower pH generally had larger γ and YHONO. Soil organics played a key role in HONO formation through the photochemical uptake of NO₂ on soil surfaces. The γ showed a positive correlation with irradiation and temperature, while it exhibited a negative relationship with relative humidity (RH). YHONO inversely depended on the soil mass (0.32–3.25 mg cm⁻²), and it positively relied on the irradiance and RH (7%–22%). There was a maximum value for YHONO at 298 K. Based on the experimental results, HONO source strengths from heterogeneous photochemical reaction of NO₂ on the soil surfaces were estimated to be 0.2–2.7 ppb h⁻¹ for a mixing layer height of 100 m, which could account for the missing daytime HONO sources in most areas.

The physico-chemical properties of submicron aerosols were measured in the spring of 2018 and 2019 over the Yellow Sea onboard the Gisang 1 research vessel. Aerosol number concentrations in 2019 were slightly higher than those in 2018, and the mean number concentrations of particles larger than 10 nm and cloud condensation nuclei (CCN) at 0.6% supersaturation (S) in spring 2019 were 7312 ± 3807 cm⁻³ and 4816 ± 1692 cm⁻³, respectively. Aerosol concentrations in June were lower than those in April and May, which was considered to be due to the East Asian summer monsoon. Aerosol number concentrations and size distributions were significantly influenced by meteorological conditions, such as wind and relative humidity. Aitken and accumulation mode particles dominated the aerosol number size distributions over the Yellow Sea. A distinct new particle formation (NPF) and growth event was observed, the spatial extent of which was estimated to cover at least 200 km × 400 km of the Yellow Sea. The general characteristics of NPF and growth over the Yellow Sea were similar to those in rural areas. Aerosol number concentrations below 1000 cm⁻³ were recorded on extremely clean days. A CCN closure experiment conducted using previous measurement data showed good results, indicating that CCN concentrations can be estimated with good accuracy, and the hygroscopicity over the Yellow Sea was similar to that of aged continental aerosols.

Studies of algal bloom early warning systems have rarely paid attention to the dynamics of excessive proliferation of phytoplankton (EPP), which occurs prior to algal blooms, or to the sensitivity of a lake to EPP based on multiple environmental factors. In this study, we investigated EPP dynamics in large lakes and identified major factors that influenced the lake's vulnerability to EPP, to improve algal bloom early warning systems. High temporal moderate resolution imaging spectroradiometer (MODIS) images and multi-source daily site monitoring data of large lakes in the middle–lower Yangtze River basin were analyzed. Then, the floating algal index (FAI) and resource use efficiency (RUE) by phytoplankton were used to investigate the EPP dynamics and lake's vulnerability to EPP, respectively. Moreover, generalized linear models were used to assess the relative importance of environmental factors on RUE. The results indicate that the lakes freely connected (FC) to the Yangtze River (Dongting Lake and Poyang Lake) had lower FAIs but higher RUEs than the non-connected lakes (NC; Chaohu Lake and Taihu Lake). The key factors affecting RUE-FC were standard deviation of water level within 30 days(WL30), particulate matter <10 μm(PM₁₀), and relative humidity(Hum), which explained 15.91% of the variations in RUE. The key factors affecting RUE-NC were ozone(O₃), basin normalized difference vegetation index standard deviation(BNDVISD), and dissolved oxygen(DO), which explained 35.28% of the variations in RUE. These results emphasize the importance of air quality in influencing or reflecting EPP risks in large lakes. In addition, basin vegetation and hydrological rhythms can influence NH₄⁺ through non-point source loading. Algal bloom early warning systems can be improved by routine monitoring and forecasting of potential environmental factors such as air quality and basin vegetation.