As the second largest economy in the world, China experiences severe particulate matter (PM) pollution in many of its cities. Meteorological factors are critical in determining both areal and temporal variations in PM pollution levels; understanding these factors and their interactions is critical for accurate forecasting, comprehensive analysis, and effective reduction of this pollution. This study analyzed areal and temporal variations in concentrations of PM₂.₅, PM₁₀, and PMcₒₐᵣₛₑ (PM₁₀ - PM₂.₅) and PM₂.₅ to PM₁₀ ratios (PM₂.₅/PM₁₀) and their relationships with meteorological conditions in 366 Chinese cities from January 1, 2015 to December 31, 2017. On the national scale, PM₂.₅ and PM₁₀ decreased from 48 to 42 μg m⁻³ and from 88 to 84 μg m⁻³, respectively, and the annual mean concentrations were 45 μg m⁻³ (PM₂.₅) and 84 μg m⁻³ (PM₁₀) during the time period (2015–2017). In most regions, largest PM concentrations occurred in winter. However, in northern China, in spring PMcₒₐᵣₛₑ concentrations were highest due to dust. The PM₂.₅/PM₁₀ ratio was higher in southern than in northern China. There were large regional disparities in PM diurnal variations. Generally, PM concentrations were negatively correlated with precipitation, relative humidity, air temperature, and wind speed, but were positively correlated with surface pressure. The sunshine duration showed negative and positive impacts on PM in northern and southern cities, respectively. Meteorological factors impacted particulates of different size differently in different regions and over different periods of time.

In the era of big data, a variety of factors (particularly meteorological factors) have been applied to PM2.5 concentration prediction, revealing a clear discrepancy in timescale. To capture the complicated multi-scale relationship with PM2.5-related factors, a novel multi-factor & multi-scale method is proposed for PM2.5 forecasting. Three major steps are taken: (1) multi-factor analysis, to select predictive factors via statistical tests; (2) multi-scale analysis, to extract scale-aligned components via multivariate empirical mode decomposition; and (3) PM2.5 prediction, including individual prediction at each timescale and ensemble prediction across different timescales. The empirical study focuses on the PM2.5 of Cangzhou, which is one of the most air-polluted cities in China, and indicates that the proposed multi-factor & multi-scale learning paradigms statistically outperform their corresponding original techniques (without multi-factor and multi-scale analysis), semi-improved variants (with either multi-factor or multi-scale analysis), and similar counterparts (with other multi-scale analyses) in terms of prediction accuracy.

Temperature has been associated with population health, but few studies have projected the future temperature-related years of life lost attributable to climate change. To project future temperature-related disease burden in Tianjin, we selected years of life lost (YLL) as the dependent variable to explore YLL attributable to climate change. A generalized linear model (GLM) and distributed lag non-linear model were combined to assess the non-linear and delayed effects of temperature on the YLL of non-accidental mortality. Then, we calculated the YLL changes attributable to future climate scenarios in 2055 and 2090. The relationships of daily mean temperature with the YLL of non-accident mortality were basically U-shaped. Both the daily mean temperature increase on high-temperature days and its drop on low-temperature days caused an increase of YLL and non-accidental deaths. The temperature-related YLL will worsen if future climate change exceeds 2 °C. In addition, the adverse effects of extreme temperature on YLL occurred more quickly than that of the overall temperature. The impact of low temperature was greater than that of high temperature. Men were vulnerable to high temperature compared with women. This analysis highlights that the government should formulate environmental policies to reach the Paris Agreement goal.

Particulate matter (PM) pollution in urban cities is of great concern for public health due to its global and adverse effect of human health while ecosystems function and vegetation control is an effective and eco-friendly way to alleviate PM pollution. We reviewed 150 studies conducted in 15 countries that were published between 1960 and 2016 and used a meta-analysis to examine the time trends and regional differences in leaf deposited PM of urban greening plants. The results suggested that the weekly PM leaf deposition varied markedly with both plant species and space-time and the average value was 1.71 ± 0.05 g m⁻²·wk⁻¹, and the variations occurred because of vegetation factors, characteristics of the PM source and meteorological factors. Moreover, fine particulate matter accounts for the minimum proportion of the total PM mass but its number ratio is maximum, more than 90% of the total number of particles. This meta-analysis illustrated the spatio-temporal trends and variations in PM leaf deposition and the influencing factors, which provides a scientific basis for the mechanism of PM deposition on leaf surface as well as plant selection and configuration in urban greening.

From hourly ozone observations obtained from three regions⸻Houston, Dallas, and West Texas⸻we investigated the contributions of meteorology to changes in surface daily maximum 8-h average (MDA8) ozone from 2000 to 2019. We applied a deep convolutional neural network and Shapely additive explanation (SHAP) to examine the complex underlying nonlinearity between variations of surface ozone and meteorological factors. Results of the models showed that between 2000 and 2019, specific humidity (38% and 27%) and temperature (28% and 37%) contributed the most to ozone formation over the Houston and Dallas metropolitan areas, respectively. On the other hand, the results show that solar radiation (50%) strongly impacted ozone variation over West Texas during this time. Using a combination of the Kolmogorov-Zurbenko (KZ) filter and multiple linear regression, we also evaluated the influence of meteorology on ozone and quantified the contributions of meteorological parameters to trends in surface ozone formation. Our findings showed that in Houston and Dallas, meteorology influenced ozone variations to a large extent. The impacts of meteorology on West Texas, however, showed meteorological factors had fewer influences on ozone variabilities from 2000 to 2019. This study showed that SHAP analysis and the KZ approach can investigate the contributions of the meteorological factors on ozone concentrations and help policymakers enact effective ozone mitigation policies.

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.

There is an increasing interest in spatial and temporal variation of air pollution and its association with weather conditions. We presented the spatial and temporal variation of Air Pollution Index (API) and examined the associations between API and meteorological factors during 2001–2011 in Guangzhou, China. A Seasonal-Trend Decomposition Procedure Based on Loess (STL) was used to decompose API. Wavelet analyses were performed to examine the relationships between API and several meteorological factors. Air quality has improved since 2005. APIs were highly correlated among five monitoring stations, and there were substantial temporal variations. Timescale-dependent relationships were found between API and a variety of meteorological factors. Temperature, relative humidity, precipitation and wind speed were negatively correlated with API, while diurnal temperature range and atmospheric pressure were positively correlated with API in the annual cycle. Our findings should be taken into account when determining air quality forecasts and pollution control measures.

Atmospheric pollution has become a significant challenge in Beijing metropolitan region, China. In this study, wavelet analysis and gray analysis were proposed to explore the temporal characteristics of particulate matter (PM10) and its relationships with meteorological factors during 2001–2012. The analysis indicated that air quality had got better significantly over the last decade. It was clearly interannual, seasonal, and monthly variation of atmospheric pollution, which represented that the air quality was the worst in spring, and got better in summer, subsequently tended to be more serious in autumn and winter. Generally atmospheric pressure was the most important meteorological feature influencing on PM10, followed by relative humidity and wind speed. However, the dominant meteorological factors influencing the atmospheric pollution were different in the four seasons. The results suggest that urban design and effective measures based on the relationship between meteorological factors and PM10 would be effective for improving atmospheric pollution.

PM₂.₅ is one of the major air pollutants in port cities of the Yangtze River Delta (YRD) of China. Understanding the driving factors of PM₂.₅ is essential to guide air pollution prevention and control. We selected 17 major port cities in YRD to study the driving factors of PM₂.₅ in 2019 and 2020. Generalized Additive Models were built to model the non-linear effects of single, multiple and interactions of driving factors on the variations of PM₂.₅. NO₂, SO₂ and the day of year are most strongly associated with the variation of PM₂.₅ concentration when used alone. Anthropogenic emissions play complicated roles in regulating PM₂.₅ concentration. Although the effect of cargo throughput (CT) on PM₂.₅ concentration is non-monotonic, higher PM₂.₅ levels are found to be associated with higher levels of SO₂ and CT. This work can potentially provide a scientific basis for formulating PM₂.₅ prevention and control policies in the region.

The present investigation was undertaken to understand the incidence of various biocomponents over jowar (Sorghum vulgare) field, wheat field, groundnut field and maize field. Environmental monitoring was carried out by operating continuous volumetric Tilak Air Sampler for 4 consecutive Rabi seasons, for the first time in this unexplored locality. Apart from dust particles and fungal bio-aerosols, remaining 5 biocomponents are reported in this paper which belonged to the group ‘Other types’, comprising of hypha fragments, insect scales (parts), pollen grains, trichomes (hairs) and unidentified fungal spores. From the various crop fields studied, these bio components contributed highest (15.8%) over wheat field and lowest (9.4%) over maize field to the total airspora. Airborne biocomponents obtained peak in the month of November over wheat field (17.8%) and groundnut field (16.6%), when there was a record of 22.5°C and 21.8°C mean temperature, 54.4% and 56.7% mean relative humidity and 10.4 mm and 14.5 mm rainfall respectively. Similar peak was obtained in the air over jowar field (11.6%) and maize field (11.3%) in the month of March, when there was a record of 30.6°C and 30.1°C mean temperature, 42.4% and 42.2% mean relative humidity and nil record of rainfall. Allergenic nature of hyphal fragments, insect scales and pollen grains causing allergy and allergenic ailments in human health hazards is also presented in this paper.