This study determined the effects of seasonality on air pollution in a tropical city of Southern Nigeria. This was with a view to acquiring data that would be useful in policy formulation and planning for proper manag...This study determined the effects of seasonality on air pollution in a tropical city of Southern Nigeria. This was with a view to acquiring data that would be useful in policy formulation and planning for proper management of ailments that result from seasonal variation of air pollution in the study area. Sampling for the study covered a period of six months, between mid-October 2013 and mid-April 2014. Air pollutants, taken into consideration, include particulate matter (PM0.3, 0.5, 1.0, 2.5, 5.0 and 10μm) and carbon monoxide (CO). Particulate matter was measured using a hand-held particle counter, while CO was measured with a single gas monitor (T40 Rattler). Five sampling points were selected based on stratified sampling technique, which represented five land use types monitored in the study area. Sampling was carried out twice in a week in accordance with the guidelines of Central Pollution Control Board, Delhi India. Sampling height was two meters above ground level. The student T-test was used to determine significant differences in monthly mean concentration of air pollutants across dry and wet seasons. The results revealed the dry season with mean values of 248568.19, 64639.04, 11140.21, 2810.39, 665.84, 320.80 particle counts for PM0.3, 0.5, 1.0, 2.5, 5.0 and 10μm and 3.01 ppm for CO concentration, was characterized by higher concentration of pollutants, while the rainy season with a mean values of 94728.24, 24745.69, 4338.29, 1158.11, 262.69, 131.36 particle counts for PM0.3, 0.5, 1.0, 2.5, 5.0 and 10μm and 2.70 ppm for CO concentration was characterized with less concentration of pollutants. The study concludes that seasonality significantly influences the concentration of pollutants in the city.展开更多
Land use regression （LUR） model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regre...Land use regression （LUR） model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression （MLR） equations were established based on the most significant variables for NO2 in heating season （R2 = 0.74）, and non-heating season （R2 = 0.61） in the whole study area; and PM10 in heating season （R2 = 0.72）, and non-heating season （R2 = 0.49）. Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations （resolution is 10 krn）. Intercepts of MLR equations were 0.050 （NOz, heating season）, 0.035 （NO2, non-heating season）, 0.068 （PM10, heating season）, and 0.092 （PM10, non-beating season） in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 （NO2, heating season）, 0.043 （NO2, non-heating season）, 0.087 （PM10, heating season）, and 0.096 （PMl0, non-heating season）. These intercept values might imply an area＇s background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. Rz values increased 0.09 （heating season） and 0.18 （non-heating season） for NO2, and 0.08 （heating season） and 0.04 （non-heating season） for PMl0. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NOz compared with PM10.展开更多
文摘This study determined the effects of seasonality on air pollution in a tropical city of Southern Nigeria. This was with a view to acquiring data that would be useful in policy formulation and planning for proper management of ailments that result from seasonal variation of air pollution in the study area. Sampling for the study covered a period of six months, between mid-October 2013 and mid-April 2014. Air pollutants, taken into consideration, include particulate matter (PM0.3, 0.5, 1.0, 2.5, 5.0 and 10μm) and carbon monoxide (CO). Particulate matter was measured using a hand-held particle counter, while CO was measured with a single gas monitor (T40 Rattler). Five sampling points were selected based on stratified sampling technique, which represented five land use types monitored in the study area. Sampling was carried out twice in a week in accordance with the guidelines of Central Pollution Control Board, Delhi India. Sampling height was two meters above ground level. The student T-test was used to determine significant differences in monthly mean concentration of air pollutants across dry and wet seasons. The results revealed the dry season with mean values of 248568.19, 64639.04, 11140.21, 2810.39, 665.84, 320.80 particle counts for PM0.3, 0.5, 1.0, 2.5, 5.0 and 10μm and 3.01 ppm for CO concentration, was characterized by higher concentration of pollutants, while the rainy season with a mean values of 94728.24, 24745.69, 4338.29, 1158.11, 262.69, 131.36 particle counts for PM0.3, 0.5, 1.0, 2.5, 5.0 and 10μm and 2.70 ppm for CO concentration was characterized with less concentration of pollutants. The study concludes that seasonality significantly influences the concentration of pollutants in the city.
基金supported by the Special Environmental Research Funds for Public Welfare (No. 200709048,200909005)the National Natural Science Foundation of China (No. 20677030)
文摘Land use regression （LUR） model was employed to predict the spatial concentration distribution of NO2 and PM10 in the Tianjin region based on the environmental air quality monitoring data. Four multiple linear regression （MLR） equations were established based on the most significant variables for NO2 in heating season （R2 = 0.74）, and non-heating season （R2 = 0.61） in the whole study area; and PM10 in heating season （R2 = 0.72）, and non-heating season （R2 = 0.49）. Maps of spatial concentration distribution for NO2 and PM10 were obtained based on the MLR equations （resolution is 10 krn）. Intercepts of MLR equations were 0.050 （NOz, heating season）, 0.035 （NO2, non-heating season）, 0.068 （PM10, heating season）, and 0.092 （PM10, non-beating season） in the whole study area. In the central area of Tianjin region, the intercepts were 0.042 （NO2, heating season）, 0.043 （NO2, non-heating season）, 0.087 （PM10, heating season）, and 0.096 （PMl0, non-heating season）. These intercept values might imply an area＇s background concentrations. Predicted result derived from LUR model in the central area was better than that in the whole study area. Rz values increased 0.09 （heating season） and 0.18 （non-heating season） for NO2, and 0.08 （heating season） and 0.04 （non-heating season） for PMl0. In terms of R2, LUR model performed more effectively in heating season than non-heating season in the study area and gave a better result for NOz compared with PM10.
