Majid Shafiepour Motlagh

This study explores the development of a comprehensive techno-economic model of environmental degradation based on the ReCiPe2016 approach named Financial Developed ReCiPe (FDR). The FDR considers cause-and-effect pathways of environmental degradation by ocean acidification, floods, acid rain, malnutrition, forest destruction, and waste more than the ReCiPe2016 in the midpoint and the environmental properties in the endpoint by considering tourism potential and intergenerational benefits. This model quantifies environmental degradation by the functions of fate factors (FF), effect factors (EF), exposure factors (XF), and economic impacts. These functions are developed for added cause-and-effect pathways, and the results were verified based on real studies. The uncertainties are considered by Individualist, Hierarchist, and Egalitarian perspectives, and the Monte Carlo Simulation (MCS) method is used to estimate the uncertainty level of variables. The results indicate the acceptability of the findings for the 20–1000-year infinite time horizon is about 13–38% variation. The FDR reveals significant deviations in the Hierarchist perspective compared to the ReCiPe2016; non-cancer diseases due to stratospheric ozone depletion and malnutrition by global warming are increased by approximately 17% and 13%, respectively. Each hectare of forest destruction’s impact on global warming, tourism, and timber resources equates to annual emission of 86 tons of CO₂, 426 tons of PM2.5, and 1540 tons of crude oil, respectively. The ocean acidification effects from CO₂ emissions compared to SO₂ and terrestrial acidification, contributing about 0.03% in the Hierarchist perspective. Finally, the FDR model bridges the existing gap in lifecycle impact assessment (LCIA) in energy-intensive industries such as petrochemical industries. Graphical abstract

The simultaneous effect of heat stress and air pollutants such as ozone can cause many health issues in cities. The situation exacerbates in the context of climate change and temperature rise. Furthermore, ground-level ozone, worsened by climate change, needs investigation for effective management. Therefore, this study projects heat stress and ozone levels in two Coupled Model Intercomparison Project Phase 6 (CMIP6) climate scenarios, SSP245 and SSP585. Results indicate heightened heat stress with increased ozone levels, especially in severe climate scenarios like SSP585. Besides, the study shows that in the SSP245 and SSP585 scenarios, the seasonal shift of high Heat Index (HI) values as well as high ozone concentrations is happening toward the previous months of June-July-August (JJA). High values in the HI classification and the Maximum Daily 8-hour average (MDA8) ozone happen sooner in the March-April-May (MAM) months than the expected JJA months. Furthermore, in the SSP585 scenario, the HI classification above 105 (very hot equal to danger category) is 10% high in all months in comparison to the SSP245 scenario. The study emphasizes the importance of understanding the interactions between heat stress and ozone pollution for implementing effective adaptation and mitigation measures.

In this study, the distribution and alterations of ozone concentrations in Tehran, Iran, in 2021 were investigated. The impacts of precursors (i.e., CO, NO₂, and NO) on ozone were examined using the data collected over 12 months (i.e., January 2021 to December 2021) from 21 stations of the Air Quality Control Company (AQCC). The results of monthly heat mapping of tropospheric ozone concentrations indicated the lowest value in December and the highest value in July. The lowest and highest seasonal concentrations were in winter and summer, respectively. Moreover, there was a negative correlation between ozone and its precursors. The Inverse Distance Weighting (IDW) method was then implemented to obtain air pollution zoning maps. Then, ozone concentration modeled by the IDW method was compared with the average monthly change of total column density of ozone derived from Sentinel-5 satellite data in the Google Earth Engine (GEE) cloud platform. A good agreement was discovered despite the harsh circumstances that both ground-based and satellite measurements were subjected to. The results obtained from both datasets showed that the west of the city of Tehran had the highest averaged O₃ concentration. In this study, the status of the concentration of ozone precursors and tropospheric ozone in 2022 was also predicted. For this purpose, the Box-Jenkins Seasonal Autoregressive Integrated Moving Average (SARIMA) approach was implemented to predict the monthly air quality parameters. Overall, it was observed that the SARIMA approach was an efficient tool for forecasting air quality. Finally, the results showed that the trends of ozone obtained from terrestrial and satellite observations throughout 2021 were slightly different due to the contribution of the tropospheric ozone precursor concentration and meteorology conditions.