In recent decades, population increase and urban development have led to catastrophic environmental consequences. One of the principal objectives to achieve “sustainable development” is to find suitable landfills. Due to their physical characteristics, which have led to a lack of landfill sites and closeness to water bodies, agricultural fields, and residential areas, the cities of Javanrood, Paveh, and Ravansar were chosen as the necessary research regions. On the other hand, these landfills are unable to accommodate the growing urban population. Therefore, this study attempts to develop a framework for spotting the most suitable sites for landfill construction with these three cities as case studies. For this, 10 important driving factors (9 factors and 1 constraint) in landfill site selection were generated. Second, for the fuzzy membership function, the analytic hierarchy process (AHP) method was employed for the standardization of criteria and determining the weight of the driving factors. Then, the Boolean, weighted linear combination (WLC) and ordered weighted average (OWA) methods were utilized to spot optimal sites for landfills. Finally, two suitable sites were found for landfills: site (a) was obtained from the WLC, and site (b) was obtained from OWA-low risk some trade-off (LRST) methods. Our results proved the high efficiency of multi-criteria decision-making methodology for landfill site selection.

The anthropogenic CO₂ emission is contributed to the rapid increase in CO₂ concentration. In the current study the anthropogenic CO₂ emission in the Middle East (ME) is investigated using 6 years column-averaged CO₂ dry air mole fraction (XCO₂) observation from Orbiting Carbon Observatory-2 (OCO-2) satellite. In this way, the XCO₂ anomaly ( $\mathrm{\Delta }$XCO₂) as the detrended and deseasonalized term of OCO-2XCO₂ product, was computed and compared to provide the direct space-based anthropogenic CO₂ emission monitoring. As a result, the high positive and negative $\mathrm{\Delta }$XCO₂ values have corresponded to the major sources such as oil and gas industries, and growing seasons over ME, respectively. Consequently, the Open-source Data Inventory for Anthropogenic CO₂ (ODIAC) emission and the gross primary productivity (GPP) were utilized in exploring the $\mathrm{\Delta }$XCO₂ relation with human and natural driving factors. The results showed the capability of $\mathrm{\Delta }$XCO₂ maps in detecting CO₂ emission fluctuations in defined periods were detectible in daily to annual periods. The simplicity and accuracy of the method in detecting the man-made and natural driving factors including the main industrial areas, megacities, or local changes due to COVID-19 pandemic or geopolitical situations as well as the vegetation absorption and biomass burning is the key point that provides the environmental managers and policymakers with valuable and accessible information to control and ultimately reduce the CO₂ emission over critical regions.

Atmospheric Carbon Dioxide (CO2), a significant greenhouse gas, drives climate change, influencing temperature, rainfall, and the hydrologic cycle. This alters precipitation patterns, intensifies storms, and changes drought frequency and timing of floods, impacting ecosystems, agriculture, water resources, and societies globally. Understanding how global CO2 fluctuations impact regional atmospheric CO2 levels can inform mitigation strategies and Facilitate water resources management. The study investigates how global CO2 fluctuations affect atmospheric CO2 concentrations (XCO2) in Iran from 2015 to 2020, aiming to inform mitigation strategies against climate change. XCO2 data OCO-2 satellite and CO2 surface flux data from the Copernicus Atmosphere Monitoring Service (CAMS) were analyzed. Over the 6 years, XCO2 in Iran increased steadily by 12.66 ppm, mirroring global rises. However, Iran's CO2 surface flux decreased, with slight increases in anthropogenic emissions but decreased natural and total fluxes. Monthly patterns of XCO2 and surface flux exhibited variations, with XCO2 reaching its zenith in spring and dipping to its lowest point during summer, while surface flux peaked during the summer months. The results reveal a significant discrepancy between Iran's surface CO2 flux and atmospheric XCO2 trends. While Iran's anthropogenic emissions increased barely from 2015–2020, its natural and total CO2 fluxes decreased. However, XCO2 increased steadily over this period, indicating the dominant impact of global rather than local factors on Iran's XCO2. Curbing worldwide greenhouse gas output is imperative to disrupt the current trajectory of climate change. Reporting CO2 levels can inform climate mitigation plans, reducing emissions to combat global warming and minimize global impacts on the hydrologic cycle.