Application of Statistical Distribution of PM_(10) Concentration in Air Quality Management in 5 Representative Cities of China

Objective To estimate the frequency of daily average PM10 concentrations exceeding the air quality standard （AQS） and the reduction of particulate matter emission to meet the AQS from the statistical properties （probability density functions） of air pollutant concentration. Methods The daily PM10 average concentration in Beijing, Shanghai, Guangzhou, Wuhan, and Xi＇an was measured from 1 January 2004 to 31 December 2008. The PM10 concentration distribution was simulated by using the Iognormal, Weibull and Gamma distributions and the best statistical distribution of PM10 concentration in the 5 cities was detected using to the maximum likelihood method. Results The daily PM10 average concentration in the 5 cities was fitted using the Iognormal distribution. The exceeding duration was predicted, and the estimated PMlo emission source reductions in the 5 cities need to be 56.58%, 93.40%, 80.17%, 82.40%, and 79.80%, respectively to meet the AO, S. Conclusion Air pollutant concentration can be predicted by using the PM10 concentration distribution, which can be further applied in air quality management and related policy making.

Characteristics of air pollution events over Hotan Prefecture at the southwestern edge of Taklimakan Desert, China

Hotan Prefecture is located at the southwestern edge of Taklimakan Desert, the world＇s largest shifting sand desert, of China. The desert is one of the main sources for frequent sand-dust weather events which strongly affect the air quality of Hotan Prefecture. Although this region is characterized by the highest annual mean PMlo concentration values that are routinely recorded by environmental monitoring stations across China, both this phenomenon and its underlying causes have not been adequately addressed in previous researches. Reliable pollutant PM_10 data are currently retrieved using a tapered element oscillating microbalance （TEOM） 1400a, a direct real-time monitor, while additional concentration values including for PM_2.5, sulfur dioxide （SO_2）, nitrogen dioxide （NO_2）, carbon monoxide （CO） and ozone （O_3） have been collected in recent years by the Hotan Environmental Monitoring Station. Based on these data, this paper presents a comparison of the influences of different kinds of sand-dust weather events on PM_10 （or PM_2.5） as well as the concentrations of other gaseous pollutants in Hotan Prefecture. It is revealed that the highest monthly average PM_10 concentrations are observed in the spring because of the frequent occurrence of three distinct kinds of sand-dust weather events at this time, including dust storms, blowing dust and floating dust. The floating dust makes the most significant contribution to PM_10 （or PM_2.5） concentration in this region, a result that differs from eastern Chinese cities where the heaviest PM_10 pollution occurs usually in winter and air pollution results from the excess emission of local anthropogenic pollutants. It is also shown that PM_10 concentration varies within wpical dust storms. PM_10 concentrations vary among 20 dust storm events within Hotan Prefecture, and the hourly mean concentrations tend to sharply increase initially then slowly decreasing over time. Data collected from cities in eastern China show the opposite with the hourly mean PM_10 （or PM_2.5） concentration tending to slowly increase then sharply decrease during heavy air pollution due to the excess emission of local anthropogenic pollutants. It is also found that the concentration of gaseous pollutants during sand-dust weather events tends to be lower than those cases under clear sky conditions. This indicates that these dust events effectively remove and rapidly diffuse gaseous pollutants. The analysis also shows that the concentration of SO_2 decreases gradually at the onset of all three kinds of sand-dust weather events because of rapidly increasing wind velocity and the development of favorable atmospheric conditions for diffusion. In contrast, changes in O_3 and NO_2 concentrations conformed to the opposite pattern during all three kinds of sand-dust weather events within this region, implying the inter transformation of these gas species during these events.

Optimizing neural networks by genetic algorithms for predicting particulate matter concentration in summer in Beijing

We developed and tested an improved neural network to predict the average concentration of PM10(particulate matter with diameter smaller than 10 ?m) several hours in advance in summer in Beijing.A genetic algorithm optimization procedure for optimizing initial weights and thresholds of the neural network was also evaluated.This research was based upon the PM10 data from seven monitoring sites in Beijing urban region and meteorological observation data,which were recorded every 3 h during summer of 2002.Two neural network models were developed.Model I was built for predicting PM10 concentrations 3 h in advance while Model II for one day in advance.The predictions of both models were found to be consistent with observations.Percent errors in forecasting the numerical value were about 20.This brings us to the conclusion that short-term fluctuations of PM10 concentrations in Beijing urban region in summer are to a large extent driven by meteorological conditions.Moreover,the predicted results of Model II were compared with the ones provided by the Models-3 Community Multiscale Air Quality(CMAQ) modeling system.The mean relative errors of both models were 0.21 and 0.26,respectively.The performance of the neural network model was similar to numerical models,when applied to short-time prediction of PM10 concentration.

Estimation of daily PM2.5 concentration and its relationship with meteorological conditions in Beijing

When investigating the impact of air pollution on health, particulate matter less than 2.5μm in aerodynamic diameter （PM2.5） is considered more harrnful than particulates of other sizes. Therefore, studies of PM2.5 have attracted more attention. Beijing, the capital of China, is notorious for its serious air pollution problem, an issue which has been of great concern to the residents, government, and related institutes for decades. However, in China, significantly less time has been devoted to observing PM2.5 than for PM10. Especially before 2013, the density of the PM2.5 ground observation network was relatively low, and the distribution of observation stations was uneven. One solution is to estimate PM2.5 concentrations from the existing data on PM10. In the present study, by analyzing the relationship between the concentrations of PM2.5 and PM10, and the meteorological conditions for each season in Beijing from 2008 to 2014, a U-shaped relationship was found between the daily maximum wind speed and the daily PM concentration, including both PM2.5 and PM10. That is, the relationship between wind speed and PM concentration is not a simple positive or negative correlation in these wind directions; their relationship has a complex effect, with higher PM at low and high wind than for moderate winds. Additionally, in contrast to previous studies, we found that the PM2.5/PM10 ratio is proportional to the mean relative humidity （MRH）. According to this relationship, for each season we established a multiple nonlinear regression （MNLR） model to estimate the PM2.5 concentrations of the missing periods.