Thermal heterogeneity in the proximity of municipal solid waste landfills on forest and agricultural lands

Spatial analysis to identify unauthorized municipal solid waste disposal sites in rural areas of southern Mexico

The management of solid waste in rural areas of developing countries faces significant challenges due to economic constraints and irregular human settlements. These factors often lead to the creation of unauthorized disposal sites, which pose risks to human health, ecosystems and the economy. Remote sensing and geographic information system techniques provide a means to understand the complex issues associated with inadequate municipal solid waste (MSW) disposal. This study aimed to identify unauthorized disposal sites in the rural areas of southern Quintana Roo, Mexico, by examining land surface temperature (LST) and vegetation indices as potential indicators of unauthorized final disposal sites (FDSs). The findings reveal that 13% of the study areas have a high, moderate or low probability of hosting unauthorized disposal sites. Additionally, 3 authorized final disposal sites (FDSs) were confirmed, and 20 unauthorized sites were identified. LST and the normalized difference vegetation index were effective in detecting unauthorized sites, as these areas exhibited higher temperatures and less vigorous vegetation compared to adjacent areas. The results provide valuable insights into the issues associated with inadequate waste disposal in rural areas and offer information that can help optimize MSW management and mitigate its environmental and health impacts.

Prophet time series modeling of waste disposal rates in four North American cities

In this study, three different univariate municipal solid waste (MSW) disposal rate forecast models (SARIMA, Holt-Winters, Prophet) were examined using different testing periods in four North American cities with different socioeconomic conditions. A review of the literature suggests that the selected models are able to handle seasonality in a time series; however, their ability to handle outliers is not well understood. The Prophet model generally outperformed the Holt-Winters model and the SARIMA model. The MAPE and R2 of the Prophet model during pre-COVID-19 were 4.3-22.2% and 0.71-0.93, respectively. All three models showed satisfactory predictive results, especially during the pre-COVID-19 testing period. COVID-19 lockdowns and the associated regulatory measures appear to have affected MSW disposal behaviors, and all the univariate models failed to fully capture the abrupt changes in waste disposal behaviors. Modeling errors were largely attributed to data noise in seasonality and the unprecedented event of COVID-19 lockdowns. Overall, the modeling errors of the Prophet model were evenly distributed, with minimum modeling biases. The Prophet model also appeared to be versatile and successfully captured MSW disposal rates from 3000 to 39,000 tons/month. The study highlights the potential benefits of the use of univariate models in waste forecast.

Geospatial alternatives for quantification of bio-thermal influence zone in the vicinity of a solid waste dump

Owing to the release of toxic gases, leachate and thermal emissions that originate from waste dumps, these sites significantly impact environmental sustainability. The study attempts to assess the deleterious impact of municipal solid waste (MSW) dump on surrounding forested landscape by employing geospatial technologies, which are cost and time-effective. For this purpose, temporal period ranging from 2015 to 2020, having 41 valid satellite observations has been selected for study. Firstly, the radii of intense hazardous zone and hazardous zone have been measured, as two separate parameters, which are 580 ± 30 m and 1260 ± 30 m, respectively. Secondly, average spatial extent of bio-influence zone is measured to be 1262 m while the average thermal influence zone extends up to 530 m around the MSW dumping site. A detailed analysis of influence zone variations reveals that the bio-influence zone depends on multitude of meteorological parameters, whereas the thermal influence zone relies mainly on seasonal temperature fluctuations. Moreover, the level of severity of emissions from MSW decomposition directly depends upon temperature. The long-term variability analysis of these hazardous zones reveals the stationarity of their spatial extents, signifying forest resilience. This study has proved significance of geospatial techniques as an alternate of expensive and time intensive assessment methods involving in situ measurements. So the proposed technique is beneficial for environmentalists, decision-makers and municipal authorities for analysing the extent and severity of MSW pollutants for forest community to address the problem of ecological degradation.

Integrating Geographic Information System network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level

More than half of financial resources allocated for municipal solid waste management are typically spent on waste collection and transportation. An optimized landfill siting and waste collection system can save fuel costs, reduce collection truck emissions, and provide higher accessibility with lower traffic impacts. In this study, a data-driven analytical framework is developed to optimize population coverage by landfills using network analysis and satellite imagery. Two scenarios, SC1 and SC2, with different truck travel times were used to simulate generation-site-disposal-site distances in three Canadian provinces. Under status quo conditions, Landfill Regionalization Index (LFRI) ranging from 0 to 2 population centers per landfill in all three jurisdictions. LFRI consistently improved after optimization, with average LFRI ranging from 1.3 to 2.0 population centers per landfill. Lower average truck travel times and better coverage of the population centers are generally observed in the optimized systems. The proposed analytical method is found effective in improving landfill regionalization. Under SC1 and SC2, LFRI percentages of improvement ranging from 58.3% to 64.5% and 22.7% to 59.4%, respectively. Separation distance between the generation and disposal sites and truck capacity appear not a decisive factor in the optimization process. The proposed optimization framework is generally applicable to regions with different geographical and demographical attributes, and is particularly applicable in rural regions with sparsely located population centers.

Geospatial passives for dynamic vegetation monitoring around thermal power plants

As point sources of pollution, thermal power plants (TPPs) emanate hazardous gaseous and particulate matter that are of significant detriment to surrounding biological landscapes. To provide support to ecological conservation and resource management in developing countries, this study aims to establish a cost effective and robust geospatial methodology for dynamic vegetation monitoring of local pollution zones around TPPs using passive satellite-based indicators. The extent and severity of hazardous bio-influence around four TPPs is identified and monitored for a period of 5 years, using vegetation indices (VIs). High correlations of vegetation health with distance from TPPs have also been identified, signifying the hazardous impact of TPP emissions to surrounding vegetation. Variations in behavior of zones of high pollutant concentration are observed both in space and time, as a response to local seasonal weather, nature of fuel used in TPP, and type and areal coverage of vegetation around the power plants. Winter and Monsoon seasons have been identified to create favorable conditions for sustaining high pollution concentration around TPPs, and hence, the extent of hazardous bio-influence zones in these seasons is maximum. Moreover, oil-based power plant is revealed to be associated with large radial zones of degraded vegetation around it and, therefore, poses greater ecological hazard than gas-powered TPPs. The average bio influence zone measured for the test sites has been found to be 1660 m that ranges from 1600 to 1730 m for different power plants, explaining variable behavior of the used fuel and surrounding vegetation conditions. In this way, the study stresses upon the importance of geospatial data and analytical frameworks in reliable and economical monitoring of environmental pollution associated with anthropogenic sources, using passive environmental indices derived from remote data.

Development and application of an analytical framework for mapping probable illegal dumping sites using nighttime light imagery and various remote sensing indices

Illegal dump sites (IDS) pose significant risks to human and the environment and are a pressing issue worldwide. Due to their secretive nature, the detection of IDS is costly and ineffective. In this study, an analytical framework was developed to detect probable IDSs in rural and remote areas using nighttime light (NTL) as a proxy for populated areas. An IDS probability map is produced by aggregation of Landsat-8 and Suomi NPP satellite imagery, multiple-criteria decision-making analysis, and classification tools. Six variables are considered, including modified soil adjusted index, land surface temperature, NTL, highway length, railway length, and the number of landfills. Vulnerability of the inhabitants on reserve lands was assessed using three sample regions. The method appears effective in reducing potential IDSs. Only about 7% of the 31,285 km² study area are identified as probable IDS, being classified as "very high" and "high". Landfills without permit are found more effective in lowering IDS occurrence. Spatial distributions of reserve lands and the maturity of highways network nearby may be more important than the length of railways when assessing the inhabitant vulnerability due to IDS. Highway length is the most decisive factor on IDS probability among all classes, with membership grades ranging from 0.99 to 0.55. Land surface temperature appears less effective for the identification of smaller scale IDS. NTL is more prominent on IDS probability in the "very high" class, with a membership grade of 0.80. The finding suggests that populated areas represented by NTL is a priori of IDS.

Analysis of input set characteristics and variances on k-fold cross validation for a Recurrent Neural Network model on waste disposal rate estimation

The use of machine learning techniques in waste management studies is increasingly popular. Recent literature suggests k-fold cross validation may reduce input dataset partition uncertainties and minimize overfitting issues. The objectives are to quantify the benefits of k-fold cross validation for municipal waste disposal prediction and to identify the relationship of testing dataset variance on predictive neural network model performance. It is hypothesized that the dataset characteristics and variances may dictate the necessity of k-fold cross validation on neural network waste model construction. Seven RNN-LSTM predictive models were developed using historical landfill waste records and climatic and socio-economic data. The performance of all trials was acceptable in the training and validation stages, with MAPE all less than 10%. In this study, the 7-fold cross validation reduced the bias in selection of testing sets as it helps to reduce MAPE by up to 44.57%, MSE by up to 54.15%, and increased R value by up to 8.33%. Correlation analysis suggests that fewer outliers and less variance of the testing dataset correlated well with lower modeling error. The length of the continuous high waste season and length of total high waste period appear not important to the model performance. The result suggests that k-fold cross validation should be applied to testing datasets with higher variances. The use of MSE as an evaluation index is recommended.