Fine particulate matter (PM_2.5) exposure has been linked to numerous adverse health effects, with some disadvantaged subgroups bearing a disproportionate exposure burden. Few studies have been conducted to estimate the exposure and inequality of different subgroups due to a lack of adequate characterization of disparities in exposure to air pollutants in urban areas, and a mechanistic understanding of the causes of these exposure inequalities. Based on a long-term series of PM_2.5 concentrations, this study analyzed the spatial and temporal characteristics of PM_2.5 in 31 provincial capital cities of China from 2000 to 2016 using the coefficient of variation and trend analyses. A health risk assessment of human exposure to PM_2.5 from 2000 to 2016 was then undertaken. A cumulative population-weighted average concentration method was applied to investigate exposures and inequality for education level, job category, age, gender and income population subgroups. The relationships between socioeconomic factors and PM_2.5 exposure concentrations were quantified using the geographically and temporally weighted regression model (GTWR). Results indicate that the PM_2.5 concentrations in most of the capital cities in the study experienced an increasing trend at a rate of 0.98 μg m⁻³ per year from 2000 to 2016. The proportion of the population exposed to high PM_2.5 (above 35 μg m⁻³) increased annually, mainly due to the increase of population migrating into north, east, south and central China. The higher educated, older, higher income and urban secondary industry share (SIS) subgroups suffered from the most significant environmental inequality, respectively. The per capita GDP, population size, and the share of the secondary industry played an essential role in unequal exposure to PM_2.5.

To reduce the high burden of disease caused by air pollution, the World Health Organization (WHO) released new Air Quality Guidelines (AQG) on September 22, 2021. In this study, the daily fine particulate matter (PM_2.5) and surface ozone (O₃) data of 618 cities around the world is collected from 2019 to 2022. Based on the new AQG, the number of attainment days for daily average concentrations of PM_2.5 (≤ 15 µg m⁻³) and O₃ (≤ 100 µg m⁻³) is approximately 10% and 90%, respectively. China and India exhibit a decreasing trend in the number of highly polluted days (> 75 µg m⁻³) for PM. Every year over 68% and 27% of cities in the world are exposed to harmful PM_2.5 (> 35 µg m⁻³) and O₃ (> 100 µg m⁻³) pollution, respectively. Combined with the United Nations Sustainable Development Goals (SDGs), it is found that more than 35% of the world's cities face PM_2.5‐O₃ compound pollution. Furthermore, the exposure risks in these cities (China, India, etc.) are mainly categorized as “High Risk”, “Risk”, and “Stabilization”. In contrast, economically developed cities are mainly categorized as “High Safety”, “Safety”, and “Deep Stabilization.” These findings indicate that global implementation of the WHO's new AQG will minimize the inequitable exposure risk from air pollution.

A severe haze pollution incident caused by unfavorable weather conditions and a northern air mass occurred in eastern, northern, northwestern, and southwestern China from January 15 to January 22, 2018. To comparatively analyze variations in PM_2.5 pollution, hourly monitoring data and 24h meteorological data were collected. Air quality observations revealed large spatiotemporal variation in PM_2.5 concentrations in Handan, Zhengzhou, Xian, Yuncheng, Chengdu, Xiangyang, and Jinan. The daily mean PM_2.5 concentrations ranged from 111.35 to 227.23μg·m⁻³, with concentration being highest in Zhengzhou. Hourly mean PM_2.5 concentration presented multiple U-shaped curves, with higher values at night and lower values during the day. The ratios of PM_2.5 to PM₁₀ were large in target cities and the results of multiscale geographic weighted regression model (MGWR) and Pearson correlation coefficients showed that PM_2.5 had a significant positive or negative correlation with PM₁₀, CO, NO₂, and SO₂. The concentration of PM_2.5 was closely related to the combustion of fossil fuels and other organic compounds, indicating the large contribution of secondary aerosols to PM_2.5 concentrations. The analysis of meteorological conditions showed that low temperature, low wind speed, and high relative humidity could aggravate the accumulation of regional pollutants in winter. Northwestern trajectory clusters were predominant contributions except in Jinan, and the highest PM_2.5 concentrations in target cities were associated with short trajectory clusters in winter. The potential sources calculated by Weight Potential Source Contribution Function (WPSCF) and Weight Concentration-Weighted Trajectory (WCWT) models were similar and the highest values of the WPSCF (>0.5) and the WCWT (>100μg·m⁻³) were mainly distributed in densely populated, industrial, arid, and semiarid regions.