The optimal dam site selection using a group decision-making method through fuzzy TOPSIS model

The complex and controversial task of selecting a dam site in a river basin can be successfully achieved using science-informed multi-criteria decision-making (MCDM) techniques. In this paper, we describe the application of the group fuzzy TOPSIS model for optimal ranking of the case study of Kandoleh dam sites in Kermanshah province, Iran, involving 18 input criteria. In this study, decision-making committee was made up of 20 involved decision makers. The comments of four non-biased, external experts in dam site selection were also used. The triangular fuzzy numbers were used to apply experts’ opinions on the selection criteria. In total, four alternative sites were assessed based on the technical, economic, social and environmental considerations and the data were analyzed using fuzzy TOPSIS MCDM model. Ranking results were compared with multi-criteria decision-making models, including the ELimination and Choice Expressing the REality and simple additive weighting. This logical, open and transparent framework provides a science-informed decision-making approach for complex problems such as optimal dam site selection. Finally, using sensitivity analysis, local studies and group discussions, we demonstrated the multiple benefits of the proposed novel method for a science-informed, open and transparent method for optimal ranking of the dam site candidates.     

Evaluation of air quality zone classification methods based on ambient air concentration exposure

Air quality zones are used by regulatory authorities to implement ambient air standards in order to protect human health. Air quality measurements at discrete air monitoring stations are critical tools to determine whether an air quality zone complies with local air quality standards or is noncompliant. This study presents a novel approach for evaluation of air quality zone classification methods by breaking the concentration distribution of a pollutant measured at an air monitoring station into compliance and exceedance probability density functions (PDFs) and then using Monte Carlo analysis with the Central Limit Theorem to estimate long-term exposure. The purpose of this paper is to compare the risk associated with selecting one ambient air classification approach over another by testing the possible exposure an individual living within a zone may face. The chronic daily intake (CDI) is utilized to compare different pollutant exposures over the classification duration of 3 years between two classification methods. Historical data collected from air monitoring stations in Kuwait are used to build representative models of 1-hr NO₂ and 8-hr O₃ within a zone that meets the compliance requirements of each method. The first method, the “3 Strike” method, is a conservative approach based on a winner-take-all approach common with most compliance classification methods, while the second, the 99% Rule method, allows for more robust analyses and incorporates long-term trends. A Monte Carlo analysis is used to model the CDI for each pollutant and each method with the zone at a single station and with multiple stations. The model assumes that the zone is already in compliance with air quality standards over the 3 years under the different classification methodologies. The model shows that while the CDI of the two methods differs by 2.7% over the exposure period for the single station case, the large number of samples taken over the duration period impacts the sensitivity of the statistical tests, causing the null hypothesis to fail. Local air quality managers can use either methodology to classify the compliance of an air zone, but must accept that the 99% Rule method may cause exposures that are statistically more significant than the 3 Strike method.

Implications: A novel method using the Central Limit Theorem and Monte Carlo analysis is used to directly compare different air standard compliance classification methods by estimating the chronic daily intake of pollutants. This method allows air quality managers to rapidly see how individual classification methods may impact individual population groups, as well as to evaluate different pollutants based on dosage and exposure when complete health impacts are not known.     

Forecasting air quality time series using deep learning

This paper presents one of the first applications of deep learning (DL) techniques to predict air pollution time series. Air quality management relies extensively on time series data captured at air monitoring stations as the basis of identifying population exposure to airborne pollutants and determining compliance with local ambient air standards. In this paper, 8 hr averaged surface ozone (O₃) concentrations were predicted using deep learning consisting of a recurrent neural network (RNN) with long short-term memory (LSTM). Hourly air quality and meteorological data were used to train and forecast values up to 72 hours with low error rates. The LSTM was able to forecast the duration of continuous O₃ exceedances as well. Prior to training the network, the dataset was reviewed for missing data and outliers. Missing data were imputed using a novel technique that averaged gaps less than eight time steps with incremental steps based on first-order differences of neighboring time periods. Data were then used to train decision trees to evaluate input feature importance over different time prediction horizons. The number of features used to train the LSTM model was reduced from 25 features to 5 features, resulting in improved accuracy as measured by Mean Absolute Error (MAE). Parameter sensitivity analysis identified look-back nodes associated with the RNN proved to be a significant source of error if not aligned with the prediction horizon. Overall, MAE's less than 2 were calculated for predictions out to 72 hours.

Implications: Novel deep learning techniques were used to train an 8-hour averaged ozone forecast model. Missing data and outliers within the captured data set were replaced using a new imputation method that generated calculated values closer to the expected value based on the time and season. Decision trees were used to identify input variables with the greatest importance. The methods presented in this paper allow air managers to forecast long range air pollution concentration while only monitoring key parameters and without transforming the data set in its entirety, thus allowing real time inputs and continuous prediction.     

Land use effects on phosphorus sequestration in soil aggregates in western Iran

Cultivating native lands may alter soil phosphorus (P) distribution and availability. The present study aimed to determine the distribution of P in soil aggregates for different long-term land management practices. The partitioned P in labile (L), Fe/Al-bound, Ca-bound, organic pools, and total P in four aggregate size fractions were determined for five land uses (forest, vineyard after 30 years, wetland, alfalfa, and wheat cultivated soil after 20 years). Both native land uses (forest and wetland) were distinguished by high and low amounts of large macro- and micro-aggregates, respectively, compared with disturbed soils (vineyard, alfalfa, and wheat soils). Labile P in large macro-aggregates were higher in native land use when compared with the other land uses, which led to increasing lability of P and accelerated water pollution. Soils under native conditions sequestered more Ca-bound P in large macro-aggregates than the soils in disturbed conditions. Conversion of native lands to agricultural land caused enhanced organic P storage in aggregates smaller than the 2 mm from 31.0 to 54.3 %. Soils under forest had 30 % total P more than the vineyard for the aggregates >2 mm after 30 years land use change. However, the amount of P in smaller (<2 mm) sized aggregates was increased by 29 % for the vineyard when compared with the forest. The P storage as bound Ca particles for the large macro-aggregates had negative correlation with the micro-aggregates.     

Comparison of slope stability in two Brazilian municipal landfills

The implementation of landfill gas to energy (LFGTE) projects has greatly assisted in reducing the greenhouse gases and air pollutants, leading to an improved local air quality and reduced health risks. The majority of cities in developing countries still dispose of their municipal waste in uncontrolled 'open dumps.' Municipal solid waste landfill construction practices and operating procedures in these countries pose a challenge to implementation of LFGTE projects because of concern about damage to the gas collection infrastructure (horizontal headers and vertical wells) caused by minor, relatively shallow slumps and slides within the waste mass. While major slope failures can and have occurred, such failures in most cases have been shown to involve contributory factors or triggers such as high pore pressures, weak foundation soil or failure along weak geosynthetic interfaces. Many researchers who have studied waste mechanics propose that the shear strength of municipal waste is sufficient such that major deep-seated catastrophic failures under most circumstances require such contributory factors. Obviously, evaluation of such potential major failures requires expert analysis by geotechnical specialists with detailed site-specific information regarding foundation soils, interface shearing resistances and pore pressures both within the waste and in clayey barrier layers or foundation soils. The objective of this paper is to evaluate the potential use of very simple stability analyses which can be used to study the potential for slumps and slides within the waste mass and which may represent a significant constraint on construction and development of the landfill, on reclamation and closure and on the feasibility of a LFGTE project. The stability analyses rely on site-specific but simple estimates of the unit weight of waste and the pore pressure conditions and use "generic" published shear strength envelopes for municipal waste. Application of the slope stability analysis method is presented in a case study of two Brazilian landfill sites; the Cruz das Almas Landfill in Maceio and the Muribeca Landfill in Recife. The Muribeca site has never recorded a slope failure and is much larger and better-maintained when compared to the Maceio site at which numerous minor slumps and slides have been observed. Conventional limit-equilibrium analysis was used to calculate factors of safety for stability of the landfill side slopes. Results indicate that the Muribeca site is more stable with computed factors of safety values in the range 1.6-2.4 compared with computed values ranging from 0.9 to 1.4 for the Maceio site at which slope failures have been known to occur. The results suggest that this approach may be useful as a screening-level tool when considering the feasibility of implementing LFGTE projects.     

Flow and temperature dynamics in an urban canyon under a comprehensive set of wind directions,wind speeds,and thermal stability conditions

Environmental Fluid Mechanics - Atmospheric flow and temperature dynamics in the urban roughness sublayer exhibit numerous complexities that cannot all be investigated using models or scaled-down...     

Mapping air quality zones for coastal urban centers

This study presents a new method that incorporates modern air dispersion models allowing local terrain and land–sea breeze effects to be considered along with political and natural boundaries for more accurate mapping of air quality zones (AQZs) for coastal urban centers. This method uses local coastal wind patterns and key urban air pollution sources in each zone to more accurately calculate air pollutant concentration statistics. The new approach distributes virtual air pollution sources within each small grid cell of an area of interest and analyzes a puff dispersion model for a full year’s worth of 1-hr prognostic weather data. The difference of wind patterns in coastal and inland areas creates significantly different skewness (S) and kurtosis (K) statistics for the annually averaged pollutant concentrations at ground level receptor points for each grid cell. Plotting the S-K data highlights grouping of sources predominantly impacted by coastal winds versus inland winds. The application of the new method is demonstrated through a case study for the nation of Kuwait by developing new AQZs to support local air management programs. The zone boundaries established by the S-K method were validated by comparing MM5 and WRF prognostic meteorological weather data used in the air dispersion modeling, a support vector machine classifier was trained to compare results with the graphical classification method, and final zones were compared with data collected from Earth observation satellites to confirm locations of high-exposure-risk areas. The resulting AQZs are more accurate and support efficient management strategies for air quality compliance targets effected by local coastal microclimates.

Implications: A novel method to determine air quality zones in coastal urban areas is introduced using skewness (S) and kurtosis (K) statistics calculated from grid concentrations results of air dispersion models. The method identifies land–sea breeze effects that can be used to manage local air quality in areas of similar microclimates.     

Synthesizing of magnesium and nickel nanoparticles from spent methane dry reforming catalyst using sol–gel method: process flow diagram development

Journal of Material Cycles and Waste Management - This study aims to recover Ni and Mg from spent catalysts of dry methane reforming. Huge amounts of spent catalyst are produced every year....