Influencing Factors of PM2.5 Pollution
Disaster Points of Meteorological Factors
Bibliographic Data
| ID | 17801147 |
|---|---|
| Authors | Ruiling Sun (Nanjing University of Information Science and Technology), Yi Zhou (0000-0001-6130-687X, Nanjing University of Information Science and Technology), Jie Wu (0000-0002-1389-6964, Jiangsu Institute of Quality & Standardization, Nanjing 210029, China), Zaiwu Gong (0000-0002-2273-2726, Nanjing University of Information Science and Technology, corresponding author) |
| Year | 2019 |
| Volume | 16 |
| Issue | 20 |
| Pages | 3891-3891 |
| Publication date | 2019-10-14 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Environmental Research and Public Health (JOURNAL) |
| Journal identifiers | ISSN: 1661-7827 • E-ISSN: 1660-4601 |
| Publisher | Multidisciplinary Digital Publishing Institute (PUBLISHER • CH) |
| DOI | 10.3390/ijerph16203891 |
| PMID | 31615068 |
| OpenAlex | W2980208437 |
| Language | EN |
| References cited | 39 |
A chance constrained stochastic Data Envelopment Analysis (DEA) was developed for investigating the relations between PM 2.5 pollution days and meteorological factors and human activities, incorporating with an empirical study for 13 cities in Jiangsu Province (China) to illustrate the model. This approach not only admits random input and output environment, but also allows the evaluation unit to exceed the front edge under the given probability constraint. Moreover, observing the change in outcome variables when a group of explanatory variables are deleted provides an additional strategic technique to measure the effect of the remaining explanatory variables. It is found that: (1) For 2013-2016, the influencing factors of PM 2.5 pollution days included wind speed, no precipitation day, relative humidity, population density, construction area, transportation, coal consumption and green coverage rate. In 2016, the number of cities whose PM 2.5 pollution days was affected by construction was decreased by three from 2015 but increased according to transportation and energy utilization. (2) The PM 2.5 pollution days in southern and central Jiangsu Province were primarily affected by the combined effect of the meteorological factors and social progress, while the northern Jiangsu Province was largely impacted by the social progress. In 2013-2016, at different risk levels, 60% inland cities were of valid stochastic efficiency, while 33% coastal cities were of valid stochastic efficiency. (3) The chance constrained stochastic DEA, which incorporates the data distribution characteristics of meteorological factors and human activities, is valuable for exploring the essential features of data in investigating the influencing factors of PM 2.5
Air pollution · Data envelopment analysis · Econometrics · Geography · Meteorology · Statistics · Wind speed · Advanced Technologies in Various Fields · Air Quality and Health Impacts · Environmental Science · Mathematics · Urban Transport and Accessibility · Ecology · Pollution
The Effect of Economic Growth, Urbanization, and Industrialization on Fine Particulate Matter (PM 2.5 ) Concentrations in China
Impact of urbanization level on urban air quality
Status and chemical characteristics of ambient PM2.5 pollutions in China
Spatio–Temporal Relationship and Evolvement of Socioeconomic Factors and PM2.5 in China During 1998–2016
High Contributions of Secondary Inorganic Aerosols to PM2.5 under Polluted Levels at a Regional Station in Northern China
Spatiotemporal Changes in PM2.5 and Their Relationships with Land-Use and People in Hangzhou
Effects of Meteorological Conditions on PM2.5 Concentrations in Nagasaki, Japan
Fine-Scale Source Apportionment Including Diesel-Related Elemental and Organic Constituents of PM2.5 across Downtown Pittsburgh
| Citation velocity | historical |
|---|---|
| Highly cited | No |