Table 10 Examining the current study in light of other research.
Reference | Resolution | Pollutant | Objective | Result |
|---|---|---|---|---|
7 | 200 m 400 m 800 m 1600 m | NOx | Identify the most appropriate spatial resolution for epidemiological studies | A spatial resolution of 200–400 m is appropriate for urban centers, whereas a finer spatial resolution may be acceptable for rural areas (about 1.6 km) |
9 | 1 km 3 km | NH3, NO2, SO2, HNO3 | correlation between concentration and emission flux is computed | The correlation of NH3 was remarkable in the 1*1 km mesh but declined in the 3*3 km mesh. The correlation for NO2 was higher for the 3*3 km mesh than for the 11 km mesh. The correlation between HNO3 and SO2 was slightly different with both meshes. |
10 | 1 km 3 km | SO2, NO2, NO, CO, PM10 | Investigate the effect of spatial resolution on air quality simulation with WRF/CMAQ modeling in different areas | At fine resolution, the urban area is better forecasted. Furthermore, the resolution had little influence in rural areas. The fine grid was successful in anticipating pollution levels in the industrial sector. |
63 | 4 km 12 km | PM2.5 | Explore the sensitivity of Community Multiscale Air Quality forecast accuracy in relation to horizontal grid resolutions | Model findings with a resolution of 12 km fared better than those at 4. (54.41% for 4 km and 52.28% for 12 km) |
64 | 1 km | PM2.5 | Using learning algorithms (neural network, RF, and gradient boosting) and predictor variables, daily PM2.5 levels were estimated at a resolution of 1 km x 1 km across the contiguous US. | The models performed well at concentrations up to 60 µg/m3. they used a trained PM2.5 model and predictor variables to predict daily PM2.5 levels in every 1 km×1 km grid cell in the contiguous United States from 2000 to 2015. they used localized land-use variables within 1 km × 1 km grids to downscale PM2.5 predictions to 100 m × 100 m grid cells. |
62 | 100 m 200 m 300 m | PM10, NO2 | Evaluating air quality in larger urban areas with high spatial resolution and, at the same time, with possible mathematical resources and time demand | Comparative research demonstrated that all grids produce similar results for the geographical distribution of PM10 and NO2 concentrations, with notable changes in magnitude and processing time. The source apportionment study found that industrial sources and road transport significantly contribute to NO2 and PM10 concentrations, respectively. |
65 | 4 m 200 m 1 km | PM10, NO2 | Evaluating three alternative modeling strategies for estimating yearly NO2 and PM10 levels in a large urban region. Evaluating the effect of various spatial resolutions on the accuracy and distribution of air pollutant concentration estimations. Investigating the relationship between air pollution exposure (at different geographical resolutions and with different models) and various mortality outcomes, such as natural-cause, cardiovascular (CVD), and respiratory (RESP) mortality. | The research employed three modeling techniques: the chemical transport model (CTM) at 1 km resolution, the land-use random forest (LURF) approach at 200 m resolution, and the micro-scale Lagrangian particle dispersion model (PMSS) with building effects at 4 m resolution, with results post-processed at various buffer sizes (12, 24, 52, 100, and 200 m). The research project discovered differing distributions of NO2 and PM10 concentrations among models and spatial resolutions. Higher resolution models (e.g., PMSS at 4 m) detected more localized variations in pollutant concentrations, whereas lower resolution models (e.g., CTM at 1 km) offered larger regional estimates. |
This study | Temporal: daily 3 days 4 days 6 days Spatial: 500 m 750 m 1000 m | PM NOx | Investigate the optimal spatial and Temporal Resolution and predicting the distribution of the pollutants | In temporal modeling, utilizing auto-correlation resulted in better accuracy for the PM10 (3 days) and PM2.5 (6 days), and lower accuracy for NOx (4 days). So, the inclusion of auto-correlation has a negative impact on NOx modeling and prediction. In spatial modeling the result for PM with a resolution of 1000 m is better than 750 and 500 m and required substantially less computing effort. Also, Modeling accuracy for NOx with a resolution of 500 m is greater that other resolutions. |
