Studies on Online Measurement and Modeling of PM2.5 and PM10 Released from the Coal Fired Boilers

Abstract

The prime focus of the research was to carry out online measurement and validation of PM2.5 and PM10 released through stationary sources, which are more hazardous to human beings as fine particulate because they are easily inhaled and accumulated in the respiratory system. Preliminary studies were conducted to validate and establish a relationship between the online laser scattering method (OLSM) and the beta attenuation method (BAM) for ambient air monitoring. The instruments were placed under identical conditions as per Central Pollution Control Board (CPCB) guidelines to measure PM2.5 and PM10 in ambient air. The daily and weekly trend analyses of PM2.5 and PM10 were measured every 1 minute and 1-hour employing OLSM and BAM, respectively. The preliminary studies estimated a good relationship between the OLSM and BAM for the correlation coefficient of 0.9818 with an R2 value is 0.9639 for PM2.5. Similarly, relationship between the OLSM and BAM for the correlation coefficient of 0.966 with an R2 value is 0.9332 for PM10. The studies proved that OLSM may be an alternative to BAM for ambient air monitoring and can produce reliable real-time particulate matter measurement. Further, PM, PM2.5, and PM10 released through the stationary sources were measured using Particle Size Distribution Method (PSDM), USEPA 201a, and OLSM to establish the relationship between PM to PM2.5 and PM to PM10 because the existing norms are in terms of PM in India. In addition, to check the effect of air pollution control equipment on the size of particles removed, a particle size analyzer was used to analyze the samples withdrawn from locations such as before cyclone, after cyclone and after Electrostatic Precipitator (ESP). It is observed that as air pollution control measures increased from the cyclone to ESP, the mass fraction of PM2.5 and PM10 has increased because the cyclone separator removes larger particles than PM2.5 and PM10 and ESP VIII removes the remaining larger particles along with smaller particles. As a result, the average mass fraction of PM2.5 and PM10 measured before the cyclone is found to be 8 % and 36 %, respectively. Whereas, after ESP, it was 27 % and 84 % of PM2.5 and PM10, respectively. Similar readings were taken by varying operating parameters such as boiler capacity coal feed rate, ash content, and boiler temperature using different analytical methods, i.e., USEPA 201a, and OLSM. The study shows that the concentration of PM2.5 and PM10 measured using OLSM was higher than that measured using the USEPA 201a method. Although this observation may be attributed to the efficiency of cyclones fitted in the USEPA 201a instrument, it may be possible that PM2.5 and PM10 particles are passing through it. Furthermore, the EIA study requires PM2.5 and PM10 data to predict ambient air quality, it is essential o establish a relationship between PM and PM2.5 and PM and PM10. This relationship may help to predict PM2.5 and PM10 from the data of PM and may be very useful for stakeholders to save time and money. Therefore, an attempt was made to establish the relationship of PM with PM2.5 and PM10. The samples were taken from the stack (after ESP) us the USEPA 201a method (PM2.5 and PM10) and USEPA 17 method under different process conditions, based on which the relationship was proposed. Predicted PM2.5 and PM10 values obtained using the proposed linear model were compared with actual PM2.5 and PM10 measured employing the USEPA 201a method to validate the proposed linear model. It was observed that predicted PM2.5 and PM10 and actual PM2.5 and PM10 were correlated with 0.9593 R2 for PM2.5 and 0.9715 R2 for PM10. The results are encouraging, reflect a good fit, and show a higher R2 value. The estimation of the maximum ground level concentration of PM2.5 and PM10 released through a single stack, and multiple stacks were carried out employing AERMOD view version 9.3 up to 10 km radius from the source. The results exhibited that the IX concentration of PM2.5 and PM10 were 0.905 μg/m3 and 1.206 μg/m3, respectively, at a distance of 0.4 km and 1350 from the source. In addition, the data for 37 stacks of the region collected and emission rates of PM2.5 and PM10 were estimated for the area source. The PM2.5 and PM10 are 1.22 μg/m3 and 1.58 μg/m3, respectively. The AERMOD estimated a rise of 33 % in PM2.5 and 31 % in PM10 for the given data. Finally, the source apportionment study data were used to calculate the maximum allowable stacks in any specific industrial area. Considering the case of PM emitted through boiler stacks, the carrying capacity of the ambient air for industries in terms of PM10 is 11.5 μg/m3. Therefore, the output of the 37 different boilers stacks located at different places was considered as one combined area source, and the overall pollution load for the particular region was calculated. Furthermore, the carrying capacity of the industrial area is calculated in terms of the maximum allowable stationary source by fixing the allowable limits of PM, i.e., as per the regulatory norms of the region. Keywords: PM2.5, PM10, Measurement methods, Stationary sources, modeling, Validation