Spatiotemporal Characterization of Ambient PM_(2.5) Concentrations in Shandong Province (China)

摘要

China experiences severe particulate matter (PM) pollution problems closely linked to its rapid economic growth. Advancing the understanding and characterization of spatiotemporal air pollution distribution is an area where improved quantitative methods are of great benefit to risk assessment and environmental policy. This work uses the Bayesian maximum entropy (BME) method to assess the space-time variability of PM_(2.5) concentrations and predict their distribution in the Shandong province, China. Daily PM_(2.5) concentrations obtained at air quality monitoring sites during 2014 were used On the basis of the space-time PM_(2.5) distributions generated by BME, we performed three kinds of querying analysis to reveal the main distribution features. The results showed that the entire region of interest is seriously polluted (BME maps identified heavy pollution clusters during 2014). Quantitative characterization of pollution severity included both pollution level and duration. The number of days during which regional PM_(2.5) exceeded 75, 115, 150, and 250 μg m~(-3) varied: 43-253,13-128,4-66, and 0-15 days, respectively. The PM_(2.5) pattern exhibited an increasing trend from east to west, with the western part of Shandong being a heavily polluted area (PM_(2.5) exceeded 150 μg m~(-3) during long time periods). Pollution was much more serious during winter than during other seasons. Site indicators of PM_(2.5) pollution intensity and space-time variation were used to assess regional uncertainties and risks with their interpretation depending on the pollutant threshold. The observed PM_(2.5) concentrations exceeding a specified threshold increased almost linearly with increasing threshold value, whereas the relative probability of excess pollution decreased sharply with increasing threshold.