پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 161 |
| تعداد شماره‌ها | 6,532 |
| تعداد مقالات | 70,504 |
| تعداد مشاهده مقاله | 124,123,139 |
| تعداد دریافت فایل اصل مقاله | 97,231,194 |
تعیین تغییرات مکانی و زمانی آلایندههای گوگرد دی اکسید، نیتروژن دی اکسید و انواع ذرات معلق با استفاده از تکنیک‌های GIS در تهران | ||
| محیط شناسی | ||
| مقاله 13، دوره 40، شماره 3، آذر 1393، صفحه 723-738 اصل مقاله (861.4 K) | ||
| نوع مقاله: مقاله پژوهشی | ||
| شناسه دیجیتال (DOI): 10.22059/jes.2014.52216 | ||
| نویسندگان | ||
| علیرضا نورپور* 1؛ سید محمد علی فیض2 | ||
| 1دانشیار گروه مهندسی محیط زیست، دانشکدۀ محیط زیست، دانشگاه تهران | ||
| 2دانشجوی کارشناسی ارشد مهندسی محیط زیست، دانشکدۀ محیط زیست، دانشگاه تهران | ||
| چکیده | ||
| یکی از معضلات اساسی شهرهای بزرگ جهان پدیدۀ آلودگی هواست. ایران به خصوص کلان‌شهر تهران نیز از این پدیده مصون نیست، به طوری که سالیانه شاهد خسارات مالی، جانی و اجتماعی متأثر از این نوع آلودگی‌ها هستیم. آلودگی هوای این شهر اغلب ناشی از منابع ثابت و متحرک آلوده‌کننده است و مهم‌ترین آلایندههای تهران را ذرات معلق PM10 و PM2.5، CO، O3، SO2 و NO2 دربر می‌گیرند. از این رو در تخمین حاضر ابتدا روش‌های مختلف درونیابی برای تولید نقشه‌های کیفیت هوای حاصل از آلایندههای ذرات معلق، دی اکسید گوگرد و دی اکسید نیتروژن ارزیابی شدند. سپس، با استفاده از روش‌های درونیابی بهینه، نقشه‌های کیفیت هوای حاصل از این آلاینده‌ها برای همۀ روزهای سال 1391 تولید شد. همچنین، از آنجا که آلودگی هوا با عوامل متعددی از قبیل توپوگرافی، اقلیم، جمعیت، شبکۀ حمل و نقل و حجم ترافیک در ارتباط است با استفاده از روش LUR مهم‌ترین عوامل مؤثر در تولید هر یک از این آلاینده‌ها تعیین و در ادامه با استفاده از همین روش به مدل‌سازی آن‌‌‌ها در بهار پرداخته شد. نتایج نشان داد که برای درونیابی داده‌های آلایندههای دی اکسید گوگرد و دی اکسید نیتروژن روش کوکریجینگ همراه سه پارامتر دما، جهت و سرعت باد مناسب‌ترین روش به شمار می‌رود، در حالی که برای آلایندههای ذرات معلق PM10 و PM2.5 روش Spline نتایج بهتری را ارائه می‌دهد. همچنین، نتایج مدل LUR نشان داد که مهم‌ترین عامل مؤثر در آلایندههای SO2، NO2 و ذرات معلق PM2.5 حجم ترافیک است، در حالی که مهم‌ترین عامل مؤثر در آلایندۀ ذرات معلق PM10 اماکن صنعتی است. | ||
| کلیدواژه‌ها | ||
| آلودگی هوا؛ درونیابی؛ ذرات معلق؛ رگرسیون کاربری اراضی؛ GIS | ||
| عنوان مقاله [English] | ||
| Determination of the Spatial and Temporal Variation of SO2, NO2 and Particulate Matter Using GIS Techniques and Estimation of Concentration Modeling with LUR Method, Case Study: Tehran City | ||
| نویسندگان [English] | ||
| Alireza Noorpoor1؛ Seyed Mohammad Ali Feiz2 | ||
| 1Associate Professor, Graduate Faculty of Environment, University of Tehran (UT), Tehran, Iran | ||
| 2MSc. Student, Environmental Engineering, Graduate Faculty of Environment, University of Tehran, Iran | ||
| چکیده [English] | ||
| Introduction Studies about the health effects of long-term average exposure to outdoor air pollution have played an important role in the recent health impact assessments. Exposure assessment for epidemiological studies of long-term exposure to ambient air pollution remains a difficult challenge because of substantial small-scale spatial variation. Current approaches for assessing intra-urban air pollution contrasts include the use of exposure indicator variables, interpolation methods, dispersion models and land-use regression (LUR) models. LUR models have been increasingly used in the past few years. Land-use regression combines monitoring of air pollution at typically 20-100 locations, spread over the study area, and development of stochastic models using predictor variables usually obtained through Geographic Information Systems (GIS). Significant predictor variables include various traffic representations, population density, land use, physical geography (e.g. altitude) and climate. Land-use regression methods have generally been applied successfully to model annual mean concentrations of SO2, NO2, PM10, PM2.5, the soot content of PM and VOCs in different environments, including European and North-American cities. The performance of the method in urban areas is typically better or equivalent to geo-statistical methods, such as kriging, and dispersion models. Further developments of the land-use regression method have more focus on developing models. This can be transferred to other areas and include of additional predictor variables such as wind direction or emission data and further exploration of focal sum methods. Models that include a spatial and a temporal component are of interest for (e.g. birth cohort) the studies that require exposure variables on a finer temporal scale. There is a strong need for validation of LUR models with personal exposure monitoring. Materials & Methods This study developed average exposure estimates of one season for Sulfur dioxide (SO2), nitrogen dioxide (NO2) and Particulate Matter (PM) in Tehran in 1391. The averages exposures were constructed by first developing land use regression (LUR) models of spatial variation in annual average PM, SO2 and NO2. Data were collected from 42 locations in the Tehran City Community Air Survey and emissions source data near monitors. The annual average concentrations from the spatial models were adjusted to account for city-wide temporal trends using the time series derived from regulatory monitors. Models were developed using season 1 data and validated using season 2 data. Average exposures were then estimated for three buffers of maternal address and were averaged into the last four weeks, the trimesters, and the entire period of gestation. We characterized temporal variation of exposure estimates, correlation between PM, NO2, SO2 and the correlation of exposures across trimesters. Results and Discussion The LUR models of average annual concentrations explained a substantial amount of the spatial variation (R2 = 0.47 for SO2), (R2 = 0.51 for NO2), (R2 = 0.71 for PM10) and (R2 = 0.47 for PM2.5). The relative contribution of temporal versus spatial variations in the estimated exposures is varied by time window. The difference in seasonal cycle of these pollutants resulted in different patterns of correlations in the estimated exposures across trimesters. Table 1 shows Spearrman correlation results with wind direction, wind velocity and temperature. Table 1. Spearrman’s correlation results Pollutant Temperature Wind velocity Wind direction SO2 -0.083 -0.081 -0.085 sig. 0.05 0.05 0.05 NO2 -0.320 -0.302 -0.98 sig. 0.000 0.000 0.041 PM10 0.055 -0.012 -0.008 sig. 0.319 0.000 0.131 PM2.5 -0.109 -0.05 -0.002 sig. 0.49 0.361 0.731 The three levels of spatial buffers did not make a substantive difference in estimated exposures. The combination of spatially resolved monitoring data, LUR models and temporal adjustment using regulatory monitoring data yielded exposure estimates for PM that performed well in validation tests. Table 2 shows RMSE of spline method results. The interaction between seasonality of air pollution and exposure intervals during pregnancy needs to be considered in the future studies. Table 2. Spline method results Pollutant MethodRBF Neighbor points RMSE Pollutant MethodRBF Neighbor points RMSE SO2 Completely Regularized Spline 42 23.46 NO2 Completely Regularized Spline 42 29.51 Spline with Tension 42 22.22 Spline with Tension 42 29.40 Multiquadric 42 33 Multiquadric 42 30.15 Inverse Multiquadric 42 25.61 Inverse Multiquadric 42 29.20 Thin Plate Spline 42 31 Thin Plate Spline 42 32 PM10 Completely Regularized Spline 42 30.30 PM2.5 Completely Regularized Spline 42 16.50 Spline with Tension 42 31.25 Spline with Tension 42 17.20 Multiquadric 42 32.17 Multiquadric 42 17.90 Inverse Multiquadric 42 32.25 Inverse Multiquadric 42 17.50 Thin Plate Spline 42 31.89 Thin Plate Spline 42 16.80 Conclusions Land-use regression methods have generally been applied successfully to model the annual mean concentrations of SO2, NO2, PM10, and PM2.5. Land-use regression methods can also be benefited from a more systematic selection and description of monitoring locations and monitoring periods. More attention to the precision of geographic data is also important. A model strategy incorporating greater knowledge of the factors related to spatial variation and focusing less on maximizing the percentage of the explained variability would probably result in the models that can more readily be transferred to other areas. Where purpose-designed monitoring is included, the cost of monitoring could probably be reduced if models were transferable. Promising new developments include the use of additional predictor variables such as wind direction data or emission data and the use of the raster GIS environment – for example, to apply focal sum methods. Models that include both a spatial and a temporal component are also of interest for studies that need exposure variables on a more detailed scale. However, it remains to be seen whether these LUR models can outperform dispersion models for shorter averaging periods. Finally, an area of interest for epidemiological research is the need for validation of LUR models with personal monitoring. The combination of spatially resolved monitoring data, LUR models and temporal adjustment using regulatory monitoring data yielded exposure estimates for PM10, PM2.5, SO2 and NO2. This is performed well in validation tests. The interaction between seasonality of air pollution and exposure intervals during pregnancy needs to be considered in the future studies. | ||
| کلیدواژه‌ها [English] | ||
| Air pollution, GIS, land use regression, particulate matter | ||
|
آمار تعداد مشاهده مقاله: 2,410 تعداد دریافت فایل اصل مقاله: 1,745 |
||