Economic evaluation of a landfill system with gas recovery for municipal solid waste management: a case study

Economic activity uses resources, which leads to waste generation. With rapid industrialisation and urbanisation, per capita solid waste generation has increased considerably. Solid waste generation data for last two decades shows an alarming increase. Owing to its improper and untimely collection, the transport and disposal of municipal solid waste poses a severe threat to various components of the environment and also to public health. This paper describes the merits and demerits of various technological aspects of solid waste management. Landfill technology, as it is the most widely employed and is regarded as the most suitable and simple mechanism, especially for tropical countries such as India, is emphasised. All possible costs and benefits and externalities are examined. A cost-benefit analysis of a landfill system with gas recovery (LFSGR) has been carried out for Mumbai city's solid waste, accounting for certain external costs and benefits, and found that it could make a huge difference of savings of about Rs. 6.366 billion (approx. $0.l40 billion) per annum with reference to the existing system of waste disposal.     

香港城市固体废物系统管理

本文以大量翔实的数据介绍了香港对城市固体废物的系统管理,即分类、收集、监测分析、处理和预测评价。香港环保署自８０ 年代开始对城市固体废物进行监测并系统管理。在全港设置了３ 个大型策略性填埋场、５ 个垃圾转运站、１ 个垃圾焚烧厂（１９９７ 年５ 月关闭） 和１个化学废物处理中心。目前,香港政府正筹建一个新的、技术先进的焚烧厂。从１９８６ ～１９９７年,这些废物处理设施处理全港废物量为８９６０ ～２４３００ｔ／ｄ,其中城市固体废物比例每年不等,最低为３０ ．７ ％ ,最高为６５ ．５ ％ 。１９９７ 年,这些废物中可循环再利用物接近５０ ％ ,出口部分达１２０ 多万吨,回收资金２０ 多亿港元。根据历年来城市固体废物量与本地生产总值（ＧＤＰ）和人口数量的密切相关性,预测出２０１１ 年城市固体废物人均产率为２ ．５６ｋｇ／ 人·ｄ, 城市固体废物量为１２８１０ ｔ／ｄ。城市固体废物系统管理紧迫而重要,香港的经验是值得我们借鉴的。     

Application of soft computing to predict water quality in wetland

Prediction of water quality is a critical issue because of its significant impact on human and ecosystem health. This research aims to predict water quality index (WQI) for the free surface wetland using three soft computing techniques namely, adaptive neuro-fuzzy system (ANFIS), artificial neural networks (ANNs), and group method of data handling (GMDH). Seventeen wetland points for a period of 14 months were considered for monitoring water quality parameters including conductivity, suspended solid (SS), biochemical oxygen demand (BOD), ammoniacal nitrogen (AN), chemical oxygen demand (COD), dissolved oxygen (DO), temperature, pH, phosphate nitrite, and nitrate. The sensitivity analysis performed by ANFIS indicates that the significant parameters to predict WQI are pH, COD, AN, and SS. The results indicated that ANFIS with Nash-Sutcliffe Efficiency (NSE = 0.9634) and mean absolute error (MAE = 0.0219) has better performance to predict the WQI comparing with ANNs (NSE = 0.9617 and MAE = 0.0222) and GMDH (NSE = 0.9594 and MAE = 0.0245) models. However, ANNs provided a comparable prediction and the GMDH can be considered as a technique with an acceptable prediction for practical purposes. The findings of this study could be used as an effective reference for policy makers in the field of water resource management. Decreasing variables, reduction of running time, and high speed of these approaches are the most important reasons to employ them in any aquatic environment worldwide.

     

Ozone levels in the empty quarter of Saudi Arabia—application of adaptive neuro-fuzzy model

In arid regions, primary pollutants may contribute to the increase of ozone levels and cause negative effects on biotic health. This study investigates the use of adaptive neuro-fuzzy inference system (ANFIS) for ozone prediction. The initial fuzzy inference system is developed by using fuzzy C-means (FCM) and subtractive clustering (SC) algorithms, which determines the important rules, increases generalization capability of the fuzzy inference system, reduces computational needs, and ensures speedy model development. The study area is located in the Empty Quarter of Saudi Arabia, which is considered as a source of huge potential for oil and gas field development. The developed clustering algorithm-based ANFIS model used meteorological data and derived meteorological data, along with NO and NO₂ concentrations and their transformations, as inputs. The root mean square error and Willmott’s index of agreement of the FCM- and SC-based ANFIS models are 3.5 ppbv and 0.99, and 8.9 ppbv and 0.95, respectively. Based on the analysis of the performance measures and regression error characteristic curves, it is concluded that the FCM-based ANFIS model outperforms the SC-based ANFIS model.     

ANFIS在混凝投药前馈控制器中应用的仿真研究

在水处理中混凝投药前馈控制器的应用效果好坏关键在于控制器是否对混凝投药过程具有良好的模型辨识能力,传统的控制器效果都不太理想,而且存在沉淀池出水浊度波动大,药剂浪费严重等问题。为了解决该问题,介绍了一种用多层前馈神经网络优化模糊逻辑系统的自适应模糊推理系统——ANFIS。它具有良好的非线性函数逼近能力,在ANFIS投药前馈控制器的设计中,运用减法聚类对样本数据进行空间划分,获取初始模糊隶属函数和模糊规则,得到ANFIS模型的初始结构。用烧杯试验历史数据进行了仿真验证,并与传统的回归模型前馈投药控制仿真比较,结果表明ANFIS投药前馈控制模型明显优于回归模型,它能够根据原水水质适时有效预测混凝投药量。     

Long short-term memory neural network and improved particle swarm optimization–based modeling and scenario analysis for municipal solid waste generation in Shanghai,China

Accurate estimations of municipal solid waste (MSW) generation are vital to effective MSW management systems. While various single-point estimation approaches have been developed, the non-linearity and multiple site-specific influencing factors associated with MSW management systems make it challenging to forecast MSW generation quantities precisely. To address these concerns, this study developed a two-stage modeling and scenario analysis procedure for MSW generation and taking Shanghai as a test case demonstrated its viability. In the first stage, nine influencing factors were selected, and a hybrid novel forecasting model based on a long short-term memory neural network and an improved particle swarm optimization (IPSO-LSTM) was proposed for the forecasting of the MSW generation quantities, after which actual Shanghai data from 1980 to 2019 were used to test the performance. In the second stage, the future influencing variable values in different scenarios were predicted using an improved grey model, after which the predicted Shanghai MSW generation quantities from 2025 to 2035 were evaluated under various scenarios. It was found that (1) the proposed IPSO-LSTM had higher accuracy than the benchmark models; (2) the MSW generation quantities are expected to respectively increase to 9.971, 9.684, and 9.090 million tons by 2025 and 11.402, 11.285, and 10.240 by 2035 under the low, benchmark, and high scenarios; and (3) the MSW generation differences between the high and medium scenarios were decreasing.

     

Fuzzy AHP approach for prioritizing electronic waste management options: a case study of Tehran,Iran

Environmental Science and Pollution Research - Electronic waste (E-waste) can be considered as challenging solid waste streams especially in some developing countries, including Iran. Several...     

Comparison between Two Outdoor Smog Chamber Facilities: Matched Experiments

Solid growth is seen for the air pollution control industry for the rest of the century. Over the next twelve years purchases of particulate control equipment in the U.S. will grow at a rate considerably better than the GNP; growth rates in developing countries will be even higher. The portion of air pollution control expenditures represented by FGD systems is predicted to rise from its current level of 12 percent to 62 percent in 1991 if acid rain legislation is passed as predicted. A significant market is seen also for municipal, industrial and agricultural waste incinerators. Geography plays an important role in the strength of the industry; in the future, industrialized countries will be the sites of new designs and applications, while developing countries will be the life extension of the tried and proven designs. Industrywide, new product development is seen as an underused route to success.     

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.     

An enhanced ozone forecasting model using air mass trajectory analysis

An enhanced ozone forecasting model using nonlinear regression and an air mass trajectory parameter has been developed and field tested. The model performed significantly better in predicting daily maximum 1-h ozone concentrations during a five-year model calibration period (1993–1997) than did a previously reported regression model. This was particularly true on the 28 “high ozone” days ([O₃]>120 ppb) during the period, for which the mean absolute error (MAE) improved from 21.7 to 12.1 ppb. On the 77 days meteorologically conducive to high ozone, the MAE improved from 12.2 to 9.1 ppb, and for all 580 calibration days the MAE improved from 9.5 to 8.35 ppb. The model was field-tested during the 1998 ozone season, and performed about as expected. Using actual meteorological data as input for the ozone predictions, the MAE for the season was 11.0 ppb. For the daily ozone forecasts, which used meteorological forecast data as input, the MAE was 13.4 ppb. The high ozone days were all anticipated by the ozone forecasters when the model was used for next day forecasts.     

Evaluation of leachate production and level in municipal solid waste landfills considering secondary compression

Environmental Science and Pollution Research - Landfilled municipal solid waste (MSW) in developing countries generally produces a large amount of leachate due to high moisture content. The...     

Forecasting daily maximum surface ozone concentrations in Brunei Darussalam--an ARIMA modeling approach

A time series approach using autoregressive integrated moving average (ARIMA) modeling has been used in this study to obtain maximum daily surface ozone (O3) concentration forecasts. The order of the fitted ARIMA model is found to be (1,0,1) for the surface O3 data collected at the airport in Brunei Darussalam during the period July 1998-March 1999. The model forecasts of one-day-ahead maximum O3 concentrations have been found to be reasonably close to the observed concentrations. The model performance has been evaluated on the basis of certain commonly used statistical measures. The overall model performance is found to be quite satisfactory as indicated by the values of Fractional Bias, Normalized Mean Square Error, and Mean Absolute Percentage Error as 0.025, 0.02, and 13.14% respectively.     

Curbing dioxin emissions from municipal solid waste incineration in China: Re-thinking about management policies and practices

As one of the countries with large amounts of dioxin releases, the control of dioxins is a major challenge for China. Municipal solid waste (MSW) incineration should be considered a high priority source of dioxin emissions because it is playing an increasingly more important role in waste management. MSW incineration in China has much higher emission rates of dioxins than in the developed countries, partially resulting from the gaps in the technologies of incineration and flue gas cleaning. Moreover, the current management policies and practices also contribute significantly to the problem. We recommend lowering dioxin emission standard, strengthening fly ash management, and improving regulation enforcement to reduce dioxin releases into the environment from MSW incineration. We also propose that alternative strategies should be considered on dioxin control and call for an expansion of economic instruments in waste management to reduce waste generation and thus the need for incineration.     

Renewable energy and greenhouse gas emissions from the waste sectors of European Union member states: a panel data analysis

In the last two decades, there has been a rich debate about the environmental degradation that results from exposure to solid urban waste. Growing public concern with environmental issues has led to the implementation of various strategic plans for waste management in several developed countries, especially in the European Union. In this paper, the relationships were assessed between economic growth, renewable energy extraction and greenhouse gas (GHG) emissions in the waste sector. The Environmental Kuznets Curve hypothesis was analysed for the member states of the European Union, in the presence of electricity generation, landfill and GHG emissions for the period 1995 to 2012. The results revealed that there is no inverted-U-shaped relationship between income and GHG emissions in European Union countries. The renewable fuel extracted from waste contributes to a reduction in GHG, and although the electricity produced also increases emissions somewhat, they would be far greater if the waste-based generation of renewable energy did not take place. The waste sector needs to strengthen its political, economic, institutional and social communication instruments to meet its aims for mitigating the levels of pollutants generated by European economies. To achieve the objectives of the Horizon 2020 programme, currently in force in the countries of the European Union, it will be necessary to increase the share of renewable energy in the energy mix.     

Translating landfill methane generation parameters among first-order decay models

Landfill gas (LFG) generation is predicted by a first-order decay (FOD) equation that incorporates two parameters: a methane generation potential (L₀) and a methane generation rate (k). Because non-hazardous waste landfills may accept many types of waste streams, multiphase models have been developed in an attempt to more accurately predict methane generation from heterogeneous waste streams. The ability of a single-phase FOD model to predict methane generation using weighted-average methane generation parameters and tonnages translated from multiphase models was assessed in two exercises. In the first exercise, waste composition from four Danish landfills represented by low-biodegradable waste streams was modeled in the Afvalzorg Multiphase Model and methane generation was compared to the single-phase Intergovernmental Panel on Climate Change (IPCC) Waste Model and LandGEM. In the second exercise, waste composition represented by IPCC waste components was modeled in the multiphase IPCC and compared to single-phase LandGEM and Australia’s Solid Waste Calculator (SWC). In both cases, weight-averaging of methane generation parameters from waste composition data in single-phase models was effective in predicting cumulative methane generation from -7% to +6% of the multiphase models. The results underscore the understanding that multiphase models will not necessarily improve LFG generation prediction because the uncertainty of the method rests largely within the input parameters. A unique method of calculating the methane generation rate constant by mass of anaerobically degradable carbon was presented (k_c) and compared to existing methods, providing a better fit in 3 of 8 scenarios. Generally, single phase models with weighted-average inputs can accurately predict methane generation from multiple waste streams with varied characteristics; weighted averages should therefore be used instead of regional default values when comparing models.

Implications: Translating multiphase first-order decay model input parameters by weighted average shows that single-phase models can predict cumulative methane generation within the level of uncertainty of many of the input parameters as defined by the Intergovernmental Panel on Climate Change (IPCC), which indicates that decreasing the uncertainty of the input parameters will make the model more accurate rather than adding multiple phases or input parameters.     

Uncertainty analysis of developed ANN and ANFIS models in prediction of carbon monoxide daily concentration

This study aims to predict daily carbon monoxide (CO) concentration in the atmosphere of Tehran by means of developed artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Forward selection (FS) and Gamma test (GT) methods are used for selecting input variables and developing hybrid models with ANN and ANFIS. From 12 input candidates, 7 and 9 variables are selected using FS and GT, respectively. Evaluation of developed hybrid models and its comparison with ANN and ANFIS models fed with all input variables shows that both FS and GT techniques reduce not only the output error, but also computational cost due to less inputs. FS–ANN and FS–ANFIS models are selected as the best models considering R², mean absolute error and also developed discrepancy ratio statistics. It is also shown that these two models are superior in predicting pollution episodes. Finally, uncertainty analysis based on Monte-Carlo simulation is carried out for FS–ANN and FS–ANFIS models which shows that FS–ANN model has less uncertainty; i.e. it is the best model which forecasts satisfactorily the trends in daily CO concentration levels.     

Emissions of unintentional persistent organic pollutants from open burning of municipal solid waste from developing countries

Open burning of waste is the most significant source of polychlorinated dibenzo-para-dioxins and dibenzofurans (PCDD/PCDF) in many national inventories prepared pursuant to the Stockholm Convention on Persistent Organic Pollutants. This is particularly true for developing countries. Emission factors for POPs such as PCDD/PCDF, dioxin-like polychlorinated biphenyls (dl-PCB) and penta- and hexachlorobenzenes (PeCBz/HCB) from open burning of municipal solid waste in China and Mexico are reported herein. Six different waste sources were studied varying from urban-industrial to semi-urban to rural. For PCDD/PCDF, the emission factors to air ranged from 3.0 to 650 ng TEQ kg⁻¹ waste and for dl-PCB from 0.092 to 54 ng TEQ kg⁻¹ waste. Emission factors for PeCBz (17-1200 ng kg⁻¹ waste) and HCB (24-1300 ng kg⁻¹ waste) spanned a wide but similar range. Within the datasets there is no indication of significant waste composition effect on emission factor with the exception of significantly higher Mexico rural samples.     

Forecasting Daily Maximum Surface Ozone Concentrations in Brunei Darussalam—An ARIMA Modeling Approach

Abstract

A time series approach using autoregressive integrated moving average (ARIMA) modeling has been used in this study to obtain maximum daily surface ozone (O₃) concentration forecasts. The order of the fitted ARIMA model is found to be (1,0,1) for the surface O₃ data collected at the airport in Brunei Darussalam during the period July 1998-March 1999. The model forecasts of one-day-ahead maximum O₃ concentrations have been found to be reasonably close to the observed concentrations. The model performance has been evaluated on the basis of certain commonly used statistical measures. The overall model performance is found to be quite satisfactory as indicated by the values of Fractional Bias, Normalized Mean Square Error, and Mean Absolute Percentage Error as 0.025, 0.02, and 13.14% respectively.