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- ItemInternal and external green-blue agricultural water footprints of nations, and related water and land savings through trade(Chichester : John Wiley and Sons Ltd, 2011) Fader, M.; Gerten, D.; Thammer, M.; Heinke, J.; Lotze-Campen, H.; Lucht, W.; Cramer, W.The need to increase food production for a growing world population makes an assessment of global agricultural water productivities and virtual water flows important. Using the hydrology and agro-biosphere model LPJmL, we quantify at 0.5° resolution the amount of blue and green water (irrigation and precipitation water) needed to produce one unit of crop yield, for 11 of the world's major crop types. Based on these, we also quantify the agricultural water footprints (WFP) of all countries, for the period 1998-2002, distinguishing internal and external WFP (virtual water imported from other countries) and their blue and green components, respectively. Moreover, we calculate water savings and losses, and for the first time also land savings and losses, through international trade with these products. The consistent separation of blue and green water flows and footprints shows that green water globally dominates both the internal and external WFP (84 % of the global WFP and 94 % of the external WFP rely on green water). While no country ranks among the top ten with respect to all water footprints calculated here, Pakistan and Iran demonstrate high absolute and per capita blue WFP, and the US and India demonstrate high absolute green and blue WFPs. The external WFPs are relatively small (6 % of the total global blue WFP, 16 % of the total global green WFP). Nevertheless, current trade of the products considered here saves significant water volumes and land areas (∼263 km3 and ∼41 Mha, respectively, equivalent to 5 % of the sowing area of the considered crops and 3.5 % of the annual precipitation on this area). Relating the proportions of external to internal blue/green WFP to the per capita WFPs allows recognizing that only a few countries consume more water from abroad than from their own territory and have at the same time above-average WFPs. Thus, countries with high per capita water consumption affect mainly the water availability in their own country. Finally, this study finds that flows/savings of both virtual water and virtual land need to be analysed together, since they are intrinsically related.
- ItemMachine Learning for Determining Interactions between Air Pollutants and Environmental Parameters in Three Cities of Iran(Basel : MDPI, 2022) Rad, Abdullah Kaviani; Shamshiri, Redmond R.; Naghipour, Armin; Razmi, Seraj-Odeen; Shariati, Mohsen; Golkar, Foroogh; Balasundram, Siva K.Air pollution, as one of the most significant environmental challenges, has adversely affected the global economy, human health, and ecosystems. Consequently, comprehensive research is being conducted to provide solutions to air quality management. Recently, it has been demonstrated that environmental parameters, including temperature, relative humidity, wind speed, air pressure, and vegetation, interact with air pollutants, such as particulate matter (PM), NO2, SO2, O3, and CO, contributing to frameworks for forecasting air quality. The objective of the present study is to explore these interactions in three Iranian metropolises of Tehran, Tabriz, and Shiraz from 2015 to 2019 and develop a machine learning-based model to predict daily air pollution. Three distinct assessment criteria were used to assess the proposed XGBoost model, including R squared (R2), Root Mean Squared Error (RMSE), and Mean Absolute Error (MAE). Preliminary results showed that although air pollutants were significantly associated with meteorological factors and vegetation, the formulated model had low accuracy in predicting (R2PM2.5 = 0.36, R2PM10 = 0.27, R2NO2 = 0.46, R2SO2 = 0.41, R2O3 = 0.52, and R2CO = 0.38). Accordingly, future studies should consider more variables, including emission data from manufactories and traffic, as well as sunlight and wind direction. It is also suggested that strategies be applied to minimize the lack of observational data by considering second-and third-order interactions between parameters, increasing the number of simultaneous air pollution and meteorological monitoring stations, as well as hybrid machine learning models based on proximal and satellite data.
- ItemEffects of the COVID-19 Pandemic on Food Security and Agriculture in Iran: A Survey(Basel : MDPI AG, 2021) Rad, Abdullah Kaviani; Shamshiri, Redmond R.; Azarm, Hassan; Balasundram, Siva K.; Sultan, MuhammadThe consequences of COVID-19 on the economy and agriculture have raised many concerns about global food security, especially in developing countries. Given that food security is a critical component that is affected by global crises, beside the limited studies carried out on the macro-impacts of COVID-19 on food security in Iran, this paper is an attempt to address the dynamic impacts of COVID-19 on food security along with economic and environmental challenges in Iran. For this purpose, a survey was conducted with the hypothesis that COVID-19 has not affected food security in Iran. To address this fundamental hypothesis, we applied the systematic review method to obtain the evidence. Various evidences, including indices and statistics, were collected from national databases, scientific reports, field observations, and interviews. Preliminary results revealed that COVID-19 exerts its effects on the economy, agriculture, and food security of Iran through six major mechanisms, corresponding to a 30% decrease in the purchasing power parity in 2020 beside a significant increase in food prices compared to 2019. On the other hand, the expanding environmental constraints in Iran reduce the capacity of the agricultural sector to play a crucial role in the economy and ensure food security, and in this regard, COVID-19 forces the national programs and budget to combat rising ecological limitations. Accordingly, our study rejects the hypothesis that COVID-19 has not affected food security in Iran.