Meteorological Factors Affecting Particulate Air Pollution of a City

1961 ◽  
Vol 42 (8) ◽  
pp. 556-560 ◽  
Author(s):  
R. R. Dickson
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Meteorological Factors ◽  
Absolute Error ◽  
Graphical Analysis ◽  
Factors Affecting ◽  
Particulate Air Pollution ◽  
Day Of The Week ◽  
Matter Concentration ◽  
Pollution Concentrations

Due to its utility in handling joint functions, the method of coaxial graphical correlation is used to relate particulate air pollution at Nashville, Tennessee to various meteorological factors. The derived relationship applied to test data yielded an average absolute error of 38.3 micrograms per cubic meter and root-mean-square error of 59.3; these results are clearly superior to a climatological control forecast using seasonal average concentrations. Graphical analysis and supporting correlation-ratio computations suggest that small amounts of precipitation may be very effective in cleansing particulate matter from the atmosphere, rendering further precipitation of little consequence. The analysis emphasizes the importance of wind direction in governing air-pollution concentrations at a point, suggesting that point measurement of particulate concentrations may have little representativeness when applied to an area the size of a city. Particulate-matter concentration is found significantly correlated with day of the week (weekday-weekend groupings), offering an avenue for improvement of results.

scholarly journals How Did Distribution Patterns of Particulate Matter Air Pollution (PM2.5 and PM10) Change in China during the COVID-19 Outbreak: A Spatiotemporal Investigation at Chinese City-Level

2020 ◽  
Vol 17 (17) ◽  
pp. 6274 ◽  
Author(s):  
Zhiyu Fan ◽  
Qingming Zhan ◽  
Chen Yang ◽  
Huimin Liu ◽  
Meng Zhan
Keyword(s):  
Air Pollution ◽  
Regression Analysis ◽  
Particulate Matter ◽  
Socioeconomic Factors ◽  
Influencing Factors ◽  
Geographically Weighted Regression ◽  
Meteorological Factors ◽  
Central China ◽  
Weighted Regression ◽  
Pm2.5 And Pm10

Due to the suspension of traffic mobility and industrial activities during the COVID-19, particulate matter (PM) pollution has decreased in China. However, rarely have research studies discussed the spatiotemporal pattern of this change and related influencing factors at city-scale across the nation. In this research, the clustering patterns of the decline rates of PM2.5 and PM10 during the period from 20 January to 8 April in 2020, compared with the same period of 2019, were investigated using spatial autocorrelation analysis. Four meteorological factors and two socioeconomic factors, i.e., the decline of intra-city mobility intensity (dIMI) representing the effect of traffic mobility and the decline rates of the secondary industrial output values (drSIOV), were adopted in the regression analysis. Then, multi-scale geographically weighted regression (MGWR), a model allowing the particular processing scale for each independent variable, was applied for investigating the relationship between PM pollution reductions and influencing factors. For comparison, ordinary least square (OLS) regression and the classic geographically weighted regression (GWR) were also performed. The research found that there were 16% and 20% reduction of PM2.5 and PM10 concentration across China and significant PM pollution mitigation in central, east, and south regions of China. As for the regression analysis results, MGWR outperformed the other two models, with R2 of 0.711 and 0.732 for PM2.5 and PM10, respectively. The results of MGWR revealed that the two socioeconomic factors had more significant impacts than meteorological factors. It showed that the reduction of traffic mobility caused more relative declines of PM2.5 in east China (e.g., cities in Jiangsu), while it caused more relative declines of PM10 in central China (e.g., cities in Henan). The reduction of industrial operation had a strong relationship with the PM10 drop in northeast China. The results are crucial for understanding how the decline pattern of PM pollution varied spatially during the COVID-19 outbreak, and it also provides a good reference for air pollution control in the future.

scholarly journals Airborne pollutants and lacunar stroke: a case cross-over analysis on stroke unit admissions

2012 ◽  
Vol 4 (2) ◽  
pp. 11 ◽  
Author(s):  
Francesco Corea ◽  
Giorgio Silvestrelli ◽  
Andrea Baccarelli ◽  
Alessandra Giua ◽  
Paolo Previdi ◽  
...  
Keyword(s):  
Air Pollution ◽  
Particulate Matter ◽  
Stroke Unit ◽  
Conditional Logistic Regression ◽  
Significant Risk ◽  
Significant Risk Factor ◽  
Lacunar Stroke ◽  
Anterior Circulation ◽  
Stroke Registry ◽  
Particulate Air Pollution

Particulate air pollution is known to be associated with cardiovascular disease. The relation of particulate air pollution with cerebrovascular disease (CVD) has not been extensively studied, particularly in relation to different subtypes of stroke. A time-series study was conducted to evaluate the association between daily air pollution and acute stroke unit hospitalizations in Mantua, Italy. We analyzed 781 CVD consecutive patients living in Mantua county admitted between 2006-08. Data on stroke types, demographic variables, risk factors were available from the Lombardia Stroke Registry. Daily mean value of particulate matter with a diameter <10 mm (PM10), carbon monoxide, nitric oxide, nitrogen dioxide, sulphur dioxide, benzene and ozone were used in the analysis. The association between CVD, ischemic strokes subtypes and pollutants was investigated with a case-crossover design, using conditional logistic regression analysis, adjusting for temperature, humidity, barometric pressure and holidays. Among the 781 subjects admitted 75.7% had ischemic stroke, 11.7% haemorrhagic stroke 12.6% transient ischemic attack. In men admission for stroke was associated with PM10 [odds ratio (OR) 1.01, 95%; confidence interval (CI) 1.00-1.02; P<0.05]. According to the clinical classification, lacunar anterior circulation syndrome stroke type was related to PM10 level registered on the day of admission for both genders (OR: 1.01, 95%; CI: 1.00-1.02; P<0.05) while for total anterior circulation syndrome stroke only in men (OR: 1.04, 95%; CI 1.01-1.07; P<0.05). In conclusion, our study confirms that air pollution peaks may contribute to increase the risk of hospitalization for stroke and particulate matter seems to be a significant risk factor, especially for lacunar stroke.

scholarly journals Application of machine learning algorithms to PM2.5 concentration analysis in the state of São Paulo, Brazil

2020 ◽  
Vol 56 (1) ◽  
pp. 152-165
Author(s):  
Angela Rosa Locateli Godoy ◽  
Ana Estela Antunes da Silva ◽  
Mirelle Candida Bueno ◽  
Simone Andréa Pozza ◽  
Guilherme Palermo Coelho
Keyword(s):  
Data Mining ◽  
Air Pollution ◽  
Carbon Monoxide ◽  
Particulate Matter ◽  
Meteorological Factors ◽  
Sao Paulo ◽  
The State ◽  
São Paulo ◽  
The Relationship ◽  
Pm2.5 Concentration

Air quality monitoring data are useful in different areas of research and have varied applications, especially with a focus on the relationship between air pollution, respiratory problems, and other health hazards. The main atmospheric pollutants are: ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), and particulate matter (PM). PM is one of the main objects of study when one intends to protect people from exposure to pollutants. This study contributes to the analysis of PM2.5 in 21 stations in the state of São Paulo monitored by the Environmental Company of São Paulo State (CETESB). It employs cluster analysis, a prominent data mining method for detecting patterns and discovering similarities which is important for assessing air pollution, especially in a geographically vast area such as that of the state of São Paulo, which does not follow a single pattern. Another data mining technique (association rules) supports the analysis of the relationship between pollutants and meteorological variables, as it allows identifying changes between elements that occur together, in a wide variety of data. Our objectives include determining stations with similar behaviors and exploring the temporal variety of the pollutant as it relates to the dominant meteorological factors in the periods of high concentration. The clustering algorithm automatically separates stations according to their monthly averages of PM2.5 concentration between 2017 and 2019. The clusters of stations that showed the highest pollution rates essentially included urban centers with emissions by industries and vehicles, while those with the lowest rates were located further inland. A cyclical behavior in pollutant variation was also observed in the three years under study and for both clusters. For the months with the highest concentration of PM2.5, association rule learning was applied to connect air temperature, relative humidity, and wind speed with PM2.5 and carbon monoxide (CO) concentrations. The obtained results are useful to analyze the temporal and geolocation profiles of pollution by particulate matter, since they identify the behavior of the meteorological factors that predominate in periods of greater concentration.

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