Forcasting CO, NO2 AND PM10 using land use regression model (LUR) (case study: Tehran city)

Air quality in large cities is one of the major problems and challenges in developed and developing countries today. Occurrence and intensity of air Pollution in cities influence by various factors such as pollution Sources, meteorological factors and chemical reactions between pollutants. There are different models for predict air pollution concentrations in cities that classified in the two groups: models based on dispersion method and land use regression models (LUR). The first research on LUR model was Introduced by SAVIAH project that sponsored by the European Union,. This study was a multicenter project that is included Huddersfield and London (UK), Bilthoven (Netherlands), Prague (Czech Republic) and Warsaw (Poland). SAVIAH study aimed to develop and validate methods for analyzing the relationship between air pollution and health on a small scale. After this research, several studies use the application of this model for modeling urban air quality.
The purpose this research is forecasting the concentration of NO2, PM10 and CO in Tehran city Using land use regression in 2010. The independent variables such as land area, road network and meteorological variables used for predicting and modeling these pollutants. Although 16 cases air quality monitoring stations (AQMs) are located Tehran city limits monitored concentration of pollutants are different because of changes in traffic, land use, elevation and .... surrounding environment of air quality monitoring stations.
Land-use areas and Length of urban roads around 16 AQMs to measure with GIS techniques and used as input variables in land-use regression (LUR) models to explain pollution concentrations over space and time. These variables and meteorological variables (surface and upper) are calculated and used as explanatory variables. Pollution concentrations monitored at each AQM are used as the dependent variables.
The areas of five land uses -residential, commercial, industrial, transportation, and vegetative - are calculated using a land use map of Tehran that reached from Tehran municipality. These explanatory variables are measured over 4 buffers and 16 sectors, and wind-direction (WD) frequencies are used to calculate WD weighted urban road's length (WURL) and land uses (WLU).meteorological factors induce chemical and physical reactions, leading to the creation, destruction, and dispersion of pollutants. Hourly measured temperature, humidity, and wind speed are seasonally summarized and included the panel regression models to investigate these impacts.
Eleven circular buffers, with radii varying from 500 to 2000 meters, and sixteen sectors are delineated around each AQM. URLs for entire transportation links are calculated and then apportioned to each buffer and sector. WD frequencies are used to calculate WD-weighted URL (WURL). The same process applies to the five land uses (WLU).
A panel data set is created by the pooling of time-series and cross-sectional observations. It is also called as pooled dataset, time-series cross-sectional dataset, or longitudinal dataset. Regression models based on such data are called panel data regression models. Traffic flows are a key determinant of the concentrations of directly emitted and secondary pollutants. Since concentrations and traffic flows vary over space and time, it is proposed here to measure the spatiotemporal variations of the dependent and independent variables across geographical locations (AQMs) and hours of the day in a given region and period (season). As a reasonable proxy for traffic emissions, URL is calculated to each buffer (ring and sector). Pollution concentrations display important differences between the four seasons. In order to compare the difference impacts of the explanatory variables on pollution concentrations across the four seasons, hourly concentrations are averaged over each season, generating four seasonal hourly panel data sets, each with 384 observations (16 AQMs × 24 hours.
Four regression models are formulated and their estimates are compared. Wind-direction-weighted URLs and land-use variables are recomputed for each season, then the best-radius-buffer for a variable is used. The proposed panel regression model is expressed as:Where the indices and variables are defined as follows:p: Pollutant (PM10, NO2, CO)
i: Cross-sectional observation (1 → 16 AQMs)
t: Time-series observation (1 → 24 hours)
C: Pollution concentration
X: Explanatory variables (for URL, four land uses, and four meteorological factors)
Uit : Error term for AQM i and hour t.
Results of research show difference relation among dependent and independent variables for each pollutant. Major urban road's length in four seasons has a positive impact on concentration of three pollutants but impacts of land uses and meteorological factors are different in seasons. For example
In case CO, Residential land use area has a positive impact on concentration in four seasons that is stronger in the winter or green space area has negative impact on concentration of CO that is impressive in summer and spring.
Impact of meteorological factors on the concentration of CO is negative for wind speed and positive for the upper air index (shelter) in four seasons. Humidity impacts on CO concentration is positive at summer and negative in other seasons.
In case PM10, the industrial land use area has positive and other land uses not efficient impact of concentration in four seasons. Impact of meteorological factors on concentrations of PM10 is negative for temperature in winter and positive in other seasons. Wind speed has a negative impact in summer and spring and positive impact in autumn and winter.
In case NO2, land use area such as residential, commercial, industrial and roads have positive impacts on NO2 concentrations. Through meteorological factors, wind speed has unexpected impact on the concentration of NO2. The impact of this variable in four seasons is positive because of the chemical reactions condition among NO, O3and NO2that is prepared in low wind speed.
Evaluation of model Validity shows that is has More accurate predictions of CO and NO2 than PM10particualtly in spring and winter.
Application of land use regression model for Tehran city show the high accuracy of the model for predictions of three pollutants divided into four seasons and three pollutants . The special features of this model that to be pointed, is simplicity and Not requiring to complex data that enables using of it in Specific conditions