The environment, the climate and human health are largely exposed to gas flaring (GF) effects, releasing significant dangerous gases into the atmosphere. In the last few decades, remote sensing technology has received great attention in gas flaring investigation. The Pars Special Economic Energy Zone (PSEEZ), located in the south of Iran, hosts many natural oil/gas processing plants and petrochemical industries, making this area one of the most air-polluted zones of Iran. The object of this research is to detect GF-related thermal anomalies in the PSEEZ by applying, for the first time, the Reed-Xiaoli Detector (RXD), distinguished as the benchmark algorithm for spectral anomaly detection. The RXD performances in this research field have been tested and verified using the shortwave infrared (SWIR) bands of OLI-Landsat 8 (L8), acquired in 2018 and 2019 on the study area. Preliminary results of this automatic unsupervised learning algorithm demonstrated an exciting potential of RXD for GF anomaly detection on a monthly scale (75% success rate), with peaks in the months of January and February 2018 (86%) and December 2019 (84%). The lowest detection was recorded in October 2019 (48%). Regarding the spatial distribution of GF anomalies, a qualitatively analysis demonstrated the RXD capability in mapping the areas affected by gas flaring, with some limitations (i.e., false positives) due to possible solar radiation contribution. Further analyses will be dedicated to recalibrate the algorithm to increase its reliability, also coupling L8 and Landsat 9, as well as exploring Sentinel 2 SWIR imagery, to overcome some of the observed RXD drawbacks.

Air contamination is one of the serious problems for the environment and the health of society. Air contamination risk assessment, health effects assessment tools are used to identify the place and time of public health effects and risk prevention strategies to reduce negative health effects. Considering that there may be a relationship between factors affecting risk, in this study, interactions between them are investigated using Fuzzy Decision Test and Experimental Evaluation Method (fuzzy DEMATEL). The cause of the health air contamination risk in an industrial area are the activity of industrial complexes and port (D+R=45.98, D-R=2.09) and the presence of hydrogen sulfide (46.82, 2.61), benzene (50.87, 2.87), sulfur dioxide (43.87, 4.32) and PM10 (53.51, 5.63) and the effects of these factors will affect the amount of carbon monoxide (39.56, -4.47), ozone (38.80, -4.20), nitrogen dioxide (50.33, -1.62) and PM2.5 (44.18, -1.26). Hospitals and nursing homes (49.01, -2.58), schools (50.99, -0.59), storage and industrial facilities, roads (45.72, -0.59), urban core (51.38, -0.55) and Natural zones, agriculture and waterways (48.86, -0.17) are affected by other factors. Population density is also affected by cause factors. Population density has a two-way relationship with PM2.5. The main human and social factors include GDP per capita and urbanization rate. Hydrogen sulfide has a one-way relationship with population density. People who work in certain industries may be exposed to higher amounts of hydrogen sulfide than the general population. These industries include the production of rayon textiles, pulp and paper factories, oil and natural gas drilling operations, and wastewater treatment plants. Therefore, by controlling the activity of industrial complexes, port and the amounts of hydrogen sulfide, benzene, sulfur dioxide and PM10 in the ambient air can be prevented from the effects after that.