Spatio-temporal land-use change modeling, simulation, and prediction have become one of the critical issues in the last three decades due to uncertainty, structure, flexibility, accuracy, the ability for improvement, and the capability for integration of available models. Therefore, many types of models such as dynamic, statistical, and machine learning (ML) models have been used in the geographic information system (GIS) environment to fulfill the high-performance requirements of land-use modeling. This paper provides a literature review on models for modeling, simulating, and predicting land-use change to determine the best approach that can realistically simulate land-use changes. Therefore, the general characteristics of conventional and ML models for land-use change are described, and the different techniques used in the design of these models are classified. The strengths and weaknesses of the various dynamic, statistical, and ML models are determined according to the analysis and discussion of the characteristics of these models. The results of the review confirm that ML models are the most powerful models for simulating land-use change because they can include all driving forces of land-use change in the simulation process and simulate linear and non-linear phenomena, which dynamic models and statistical models are unable to do. However, ML models also have limitations. For instance, some ML models are complex, the simulation rules cannot be changed, and it is difficult to understand how ML models work in a system. However, this can be solved via the use of programming languages such as Python, which in turn improve the simulation capabilities of the ML models.
Landfill application is the most common approach for biowaste treatment via leachate treatment system. When municipal solid waste deposited in the landfills, microbial decomposition breaks down the wastes generating the end products, such as carbon dioxide, methane, volatile organic compounds, and liquid leachate. However, due to the landfill age, the fluctuation in the characteristics of landfill leachate is foreseen in the leachate treatment plant. The focuses of the researchers are keeping leachate from contaminating groundwater besides keeping potent methane emissions from reaching the atmosphere. To address the above issues, scientists are required to adopt green biological methods to keep the environment safe. This review focuses on the assorting of research papers on organic content and nitrogen removal from the leachate via recent effective biological technologies instead of conventional nitrification and denitrification process. The published researches on the characteristics of various Malaysian landfill sites were also discussed. The understanding of the mechanism behind the nitrification and denitrification process will help to select an optimized and effective biological treatment option in treating the leachate waste. Recently, widely studied technologies for the biological treatment process are aerobic methane oxidation coupled to denitrification (AME-D) and partial nitritation-anammox (PN/A) process, and both were discussed in this review article. This paper gives the idea of the modification of the conventional treatment technologies, such as combining the present processes to make the treatment process more effective. With the integration of biological process in the leachate treatment, the effluent discharge could be treated in shortcut and novel pathways, and it can lead to achieving "3Rs" of reduce, reuse, and recycle approach.
In an effort to determine the reason behind excellent nitrate remediation capacity at Kelantan region, a multivariate approach is employed to evaluate extent to which the influence of sea on soil geochemical composition affect variation pattern of groundwater quality. The results obtained from geochemical analysis of paleo-beach soil in coastal site at Bachok revealed multiple redox activity at different soil strata, involving both heterotrophic and autotrophic denitrification. In soil and water analysis, eight of the fourteen hydro-geochemical parameters (conductivity, temperature, soil texture, oxidation reduction potential, pH, total organic carbon, Fe, Cu, Mn, Cl-, SO42-, NO2-, NO3- and PO43-) measured using standard procedures were subjected to multivariate analysis. Evaluation of general variation pattern across the area reveals that the principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA) are in consonance with one another on apportioning three parameters (SO42-, Cl- and conductivity) to the coastal sites and two parameters (Fe and NH4+ or NO3-) to inland sites. The step forward analysis of LDA reveals four parameters in order of decreasing significance as Cl-, Fe and SO42-, while the two-way HCA identifies three clusters on location basis, respectively. In addition to the significant data reduction obtained, the results indicate that proximity to sea and location/geological-based influence are more significant than temporal-based influence in denitrification. By extension, the research reveals that influence of labile portion of natural resources is explorable for broader application in other remediation strategies.
Over the years, ethylene-diamine-tetra-acetate (EDTA) has been widely used for many purposes. However, there are inadequate phytoassessment studies conducted using EDTA in Vetiver grass. Hence, this study evaluates the phytoassessment (growth performance, accumulation trends, and proficiency of metal uptake) of Vetiver grass, Vetiveria zizanioides (Linn.) Nash in both single and mixed heavy metal (Cd, Pb, Cu, and Zn)-disodium EDTA-enhanced contaminated soil. The plant growth, metal accumulation, and overall efficiency of metal uptake by different plant parts (lower root, upper root, lower tiller, and upper tiller) were thoroughly examined. The relative growth performance, metal tolerance, and phytoassessment of heavy metal in roots and tillers of Vetiver grass were examined. Metals in plants were measured using the flame atomic absorption spectrometry (F-AAS) after acid digestion. The root-tiller (R/T) ratio, biological concentration factor (BCF), biological accumulation coefficient (BAC), tolerance index (TI), translocation factor (TF), and metal uptake efficacy were used to estimate the potential of metal accumulation and translocation in Vetiver grass. All accumulation of heavy metals were significantly higher (p < 0.05) in both lower and upper roots and tillers of Vetiver grass for Cd + Pb + Cu + Zn + EDTA treatments as compared with the control. The single Zn + EDTA treatment accumulated the highest overall total amount of Zn (8068 ± 407 mg/kg) while the highest accumulation for Cu (1977 ± 293 mg/kg) and Pb (1096 ± 75 mg/kg) were recorded in the mixed Cd + Pb + Cu + Zn + EDTA treatment, respectively. Generally, the overall heavy metal accumulation trends of Vetiver grass were in the order of Zn > Cu > Pb > Cd for all treatments. Furthermore, both upper roots and tillers of Vetiver grass recorded high tendency of accumulation for appreciably greater amounts of all heavy metals, regardless of single and/or mixed metal treatments. Thus, Vetiver grass can be recommended as a potential phytoextractor for all types of heavy metals, whereby its tillers will act as the sink for heavy metal accumulation in the presence of EDTA for all treatments.
Benzene, toluene, ethylbenzene and xylenes (BTEX) are well known hazardous volatile organic compounds (VOCs) due to their human health risks and photochemical effects. The main objective of this study was to estimate BTEX levels and evaluate interspecies ratios and ozone formation potentials (OFP) in the ambient air of urban Kuala Lumpur (KL) based on a passive sampling method with a Tenax® GR adsorbent tube. Analysis of BTEX was performed using a thermal desorption (TD)-gas chromatography mass spectrometer (GCMS). OFP was calculated based on the Maximum Incremental Reactivity (MIR). Results from this study showed that the average total BTEX during the sampling period was 66.06 ± 2.39 μg/m3. Toluene (27.70 ± 0.97 μg/m3) was the highest, followed by m,p-xylene (13.87 ± 0.36 μg/m3), o-xylene (11.49 ± 0.39 μg/m3), ethylbenzene (8.46 ± 0.34 μg/m3) and benzene (3.86 ± 0.31 μg/m3). The ratio of toluene to benzene (T:B) is > 7, suggesting that VOCs in the Kuala Lumpur urban environment are influenced by vehicle emissions and other anthropogenic sources. The average of ozone formation potential (OFP) value from BTEX was 278.42 ± 74.64 μg/m3 with toluene and xylenes being the major contributors to OFP. This study also indicated that the average of benzene concentration in KL was slightly lower than the European Union (EU)-recommended health limit value for benzene of 5 μg/m3 annual exposure.
Modeling and evaluating the behavior of particulate matter (PM10) is an important step in obtaining valuable information that can serve as a basis for environmental risk management, planning, and controlling the adverse effects of air pollution. This study proposes the use of a Markov chain model as an alternative approach for deriving relevant insights and understanding of PM10 data. Using first- and higher-order Markov chains, we analyzed daily PM10 index data for the city of Klang, Malaysia and found the Markov chain model to fit the PM10 data well. Based on the fitted model, we comprehensively describe the stochastic behaviors in the PM10 index based on the properties of the Markov chain, including its states classification, ergodic properties, long-term behaviors, and mean return times. Overall, this study concludes that the Markov chain model provides a good alternative technique for obtaining valuable information from different perspectives for the analysis of PM10 data.
Presence of copper within water bodies deteriorates human health and degrades natural environment. This heavy metal in water is treated using a promising biochar derived from rambutan (Nephelium lappaceum) peel through slow pyrolysis. This research compares the efficacies of artificial neural network (ANN), adaptive neuro-fuzzy inference system (ANFIS), and multiple linear regression (MLR) models and evaluates their capability in estimating the adsorption efficiency of biochar for the removal of Cu (II) ions based on 480 experimental sets obtained in a laboratory batch study. The effects of operational parameters such as contact time, operating temperature, biochar dosage, and initial Cu (II) ion concentration on removing Cu (II) ions were investigated. Eleven different training algorithms in ANN and 8 different membership functions in ANFIS were compared statistically and evaluated in terms of estimation errors, which are root mean squared error (RMSE), mean absolute error (MAE), and accuracy. The effects of number of hidden neuron in ANN model and fuzzy set combination in ANFIS were studied. In this study, ANFIS model with Gaussian membership function and fuzzy set combination of [4 5 2 3] was found to be the best method, with accuracy of 90.24% and 87.06% for training and testing dataset, respectively. Contribution of this study is that ANN, ANFIS, and MLR modeling techniques were used for the first time to study the adsorption of Cu (II) ions from aqueous solutions using rambutan peel biochar.
The presence of Hg, Cu, Cr, Cd, Pb, and Zn in clam (Meretrix lyrata) from the East Java Coast (EJC), Indonesia, is reported in this study. Metal levels in clam whole tissues were Zn > Cu > Cr > Pb > Cd > Hg. Cr, Cd, and Pb levels in clam tissue surpassed the tolerated limit for eating and the provisional acceptable weekly intake (PTWI) at numerous places along the EJC. The target hazard quotients (THQs) for Cr, Cd, and Pb were greater than one in several locations, indicating that these metals could be harmful to consumers (particularly non-carcinogenic impacts). Eating clams from this area may be detrimental to human health. Furthermore, target cancer risk (TCR) values for Cr and Cd were greater than 10-4 in several locations, implying that Cr and Cd could cause cancer in people over the course of a lifetime of exposure.
We have pragmatically but accurately evaluated the natural capital of a small northern town, Shimokawa, Hokkaido, Japan. The key industries are forestry, wood manufacturing, and agriculture. From an environmental perspective, Shimokawa was nominated as a Japanese FutureCity. Consequently, the total natural capital value (NCV) of the forest and agricultural lands was calculated to be 1.326 billion USD/year (or 24,161 USD/ha/year) and 44 million USD/year (or 19,692 USD/ha/year), respectively, in 2012. The sum of these NCVs was more than 7 times greater than the yearly gross production of the town, although the forest had a higher NCV because of the larger area (54,862 ha for forest area), compared with 2953 ha for agricultural area. This substantial NCV is mainly generated by sustainable forest management. The timber account showed that the annual tree growth was greater than the annual harvest of trees. The CO2 account derived from a one-year calculation showed that the town served as a CO2 sink at 107,249 t-CO2/year due to the large amount of annual tree growth and CO2 storage in the harvested wood products even if CO2 was emitted from industries and households. The forestry and wood manufacturing industries, as well as agriculture, created socioeconomic effects for the townspeople, ranging from job creation, study tours, and social welfare. This NCV accounting for Shimokawa town ensures the sustainable use of valuable environmental assets and will help other communities recognize their own NCV accounts.
Functional classification of phytoplankton could be a valuable tool in water quality monitoring in the eutrophic riverine ecosystems. This study is novel from the Bangladeshi perspective. In this study, phytoplankton cell density and diversity were studied with particular reference to the functional groups (FGs) approach during pre-monsoon, monsoon, and post-monsoon at four sampling stations in Karatoya River, Bangladesh. A total of 54 phytoplankton species were recorded under four classes, viz. Chlorophyceae (21 species) Cyanophyceae (16 species), Bacillariophyceae (15 species), and Euglenophyceae (2 species). A significantly higher total cell density of phytoplankton was detected during the pre-monsoon season (24.20 × 103 cells/l), while the lowest in monsoon (9.43 × 103 cells/l). The Shannon-Wiener diversity index varied significantly (F = 16.109, P = 000), with the highest value recorded during the post-monsoon season. Analysis of similarity (ANOSIM) identified significant variations among the three seasons (P
Phenol, an aromatic chemical commonly found in domestic and industrial effluents, upon its introduction into aquatic ecosystems adversely affects the indigenous biota, the invertebrates and the vertebrates. With the increased demand for agrochemicals, a large amount of phenol is released directly into the environment as a byproduct. Phenol and its derivatives tend to persist in the environment for longer periods which in turn poses a threat to both humans and the aquatic ecosystem. In our current study, the response of Labeo rohita to sublethal concentrations of phenol was observed and the results did show a regular decrease in biochemical constituents of the targeted organs. Exposure of Labeo rohita to sublethal concentration of phenol (22.32 mg/L) for an epoch of 7, 21 and 28 days shows a decline in lipid, protein, carbohydrate content and phosphatase activity in target organs such as the gills, muscle, intestine, liver and kidney of the fish. The present study also aims to investigate the toxic effects of phenol with special reference to the haematological parameters of Labeo rohita. At the end of the exposure period, the blood of the fish was collected by cutting the caudal peduncle with a surgical scalpel. And it was observed that the red blood corpuscle count (RBC), white blood corpuscle (WBC), haemoglobin count (Hb), packed cell volume (PCV), mean corpuscular volume (MCV), mean corpuscular haemoglobin (MCH) and mean corpuscular haemoglobin concentration (MCHC) values showed a decline after exposure to phenol for 7 days, while white blood corpuscle (WBC) shows an increased count. At 21 days and 28 days, all the haematological parameters showed a significant decrease.
The current study focused on the monitoring of pollution loads in the Kalpakkam coastal zone of India in terms of physico-chemical characteristics of sediment. The investigation took place at 12 sampling points around the Kalpakkam coastal zone for one year beginning from 2019. The seasonal change of nutrients in the sediment, such as nitrogen, phosphorus, potassium, total organic carbon, and particles size distribution, was calculated. Throughout the study period, the pH (7.55 to 8.99), EC (0.99 to 4.98 dS/m), nitrogen (21.74 to 58.12 kg/ha), phosphorus (7.5 to 12.9 kg/ha), potassium (218 to 399 kg/ha), total organic carbon (0.11 to 0.88%), and particle size cumulative percent of sediments (from 9.01 to 9.39%) was observed. A number of multivariate statistical techniques were used to examine the changes in sediment quality. The population means were substantially different according to the three-way ANOVA test at the 0.05 level. Principal component analysis and cluster analysis showed a substantial association with all indicators throughout all seasons, implying contamination from both natural and anthropogenic causes. The ecosystem of the Kalpakkam coastal zone has been affected by nutrient contamination.
Biochar derived from banana peels can be used as an alternative nutrient in the soil that can promote crop growth while reducing fertiliser usage. Biochar stability has proportional relationship to biochar residence time in the soil and potassium is one of the vital nutrients needed for plant growth. This research aims at providing optimum pyrolysis operating conditions like temperature, residence time, and heating rate using banana peels as feedstock. An electrical tubular furnace was used to conduct the pyrolysis process to convert banana peels into biochar. The elemental compositions of biochar are potassium, oxygen (O), and carbon (C) content. The O:C ratio was used as the biochar stability indicator. Analysis of results showed that operating temperature has the most remarkable effect on biochar yield, biochar stability, and biochar's potassium content. In addition, a multilayer feedforward artificial neural network model was developed for the pyrolysis process. Eleven training algorithms were selected to model the multi-input multi-output neural network (MIMO). The most suitable training algorithm was identified through four performance criterions which are root mean square error (RMSE), mean absolute error (MSE), mean absolute percentage error (MAPE), and regression (R2). The results show that the Levenberg-Marquardt backpropagation training algorithm has the lowest error. From the chosen training algorithm, neural network was trained, and optimum operating parameters for banana peel were predicted at 490 °C, 110 min, and 11 °C/min with a high yield of 47.78%, O/C ratio of 0.2393, and 14.04 wt. % of potassium.
Sustainable agriculture is important for preserving environmental health and simultaneously gaining economic profits while maintaining social and economic equity. One way to evaluate sustainable agriculture is by studying agricultural eco-efficiency (AEE). Hence, this study constructed a data-driven method to evaluate and optimize AEE with the aim of providing a basis for improving the sustainable development of regional agriculture. Sixteen cities in Anhui Province, China, were considered in the study, and the variables used were agricultural resource inputs, environmental pollution, and agricultural economic development. Agricultural non-point source pollution (NPSP) emissions were considered the undesired output to build an AEE evaluation index system. Furthermore, a data envelopment analysis (DEA) model was established to analyse AEE from the static and dynamic perspectives. The spatial development and the temporal and spatial characteristics of AEE were also analysed. In addition, we applied a random effect (RE) panel Tobit model to quantitatively analyse the influencing factors of AEE from the input perspective and then proposed reasonable suggestions for improving the sustainable development of regional agriculture. Our findings show that the overall agricultural development in the 16 cities in Anhui Province has been continuously improving, even though there is an agglomeration of spatial development in some regions. In conclusion, this study provides suggestions and references for policy makers and agricultural practitioners regarding how to improve regional AEE and promote the sustainable development of the regional agricultural economy.
This study focused on the residue detection of the herbicides triclopyr and glufosinate ammonium in the runoff losses from the Tasik Chini oil palm plantation area and the Tasik Chini Lake under natural rainfall conditions in the Malaysian tropical environment. Triclopyr and glufosinate ammonium are post-emergence herbicides. Both herbicides were foliar-sprayed on 0.5 ha of oil palm plantation plots, which were individualized by an uneven slope of 10-15%. Samples were collected at 1, 3, 7, 15, 30, 45, 60, 90, and 120 days after treatment. The concentrations of both herbicides quickly diminished from those in the analyzed sample by the time of collection. The highest residue levels found in the field surface leachate were 0.031 (single dosage, triclopyr), 0.041 (single dosage, glufosinate ammonium), 0.017 (double dosage, triclopyr), and 0.037 μg/kg (double dosage, glufosinate ammonium). The chromatographic peaks were observed at "0" day treatment (2 h after herbicide application). From the applied active ingredients, the triclopyr and glufosinate losses were 0.025 and 0.055%, respectively. The experimental results showed that both herbicides are less potent than other herbicides in polluting water systems because of their short persistence and strong adsorption onto soil clay particles.
This study investigates the presence and distribution of organochlorine pesticides in streams and the lake in the Sembrong Lake Basin in Malaysia. The catchment of Sembrong Lake has been converted to agricultural areas over the past 30 years, with oil palm plantations and modern agricultural farming being the main land use. Surface water samples were collected from eight sites comprising the stream and lake and analysed for 19 organochlorine pesticides (OCPs). In situ measurement of temperature, dissolved oxygen, pH and conductivity were also undertaken at each site. Aldrin, endrin, δ-BHC, 4,4-DDT, methoxychlor and endosulfan were the main OCPs detected in the lake basin. The total OCP concentration ranged between 5.42 and 349.2 ng/L. The most frequently detected OCPs were δ-BHC, heptachlor and aldrin. The maximum values detected were 23.0, 43.2 and 50.4 ng/L respectively. The highest concentration of OCPs was attributed to 4,4-DDT, but such high residue was rare and only detected once. Other OCP residues were low. Significant differences in the mean values were observed between lake and stream for dichlorodiphenyldichloroethylene (DDE) and α-endosulfan concentration (p
Principal component analysis (PCA) is capable of handling large sets of data. However, lack of consistent method in data pre-treatment and its importance are the limitations in PCA applications. This study examined pre-treatments methods (log (x + 1) transformation, outlier removal, and granulometric and geochemical normalization) on dataset of Mengkabong Lagoon, Sabah, mangrove surface sediment at high and low tides. The study revealed that geochemical normalization using Al with outliers removal resulted in a better classification of the mangrove surface sediment than that outliers removal, granulometric normalization using clay and log (x + 1) transformation. PCA output using geochemical normalization with outliers removal demonstrated associations between environmental variables and tides of mangrove surface sediment, Mengkabong Lagoon, Sabah. The PCA outputs at high and low tides also provided to better interpret information about the sediment and its controlling factors in the intertidal zone. The study showed data pre-treatment method to be a useful procedure to standardize the datasets and reducing the influence of outliers.
Diatom abundance, biovolume and diversity were measured over a 2-year period along the Straits of Malacca at two stations with upper (Klang) and lower (Port Dickson) states of eutrophication. Diatom abundance, which ranged from 0.2 × 10(4) to 21.7 × 10(4) cells L(-1) at Klang and 0.9 × 10(3)- 41.3 × 10(3) cells L(-1) at Port Dickson, was influenced partly by nutrient concentrations. At Klang, the diatoms were generally smaller and less diverse (H' = 0.77 ± 0.48) and predominated by Skeletonema spp. (60 ± 32% of total diatom biomass). In contrast, diatoms were larger and more diverse (H' = 1.40 ± 0.67) at Port Dickson. Chaetoceros spp. were the most abundant diatoms at Port Dickson but attributed only 48 ± 30% of total diatom biomass. Comparison of both Klang and Port Dickson showed that their diatom community structure differed and that eutrophication reduced diatom diversity at Klang. We also observed how Si(OH)4 affected the abundance of Skeletonema spp. which in turn influenced the temporal variation of diatom community at Klang. Our results highlighted how eutrophication affects diatom diversity and community structure.
Ever increasing demand for water resources for different purposes makes it essential to have better understanding and knowledge about water resources. As known, groundwater resources are one of the main water resources especially in countries with arid climatic condition. Thus, this study seeks to provide groundwater potential maps (GPMs) employing new algorithms. Accordingly, this study aims to validate the performance of C5.0, random forest (RF), and multivariate adaptive regression splines (MARS) algorithms for generating GPMs in the eastern part of Mashhad Plain, Iran. For this purpose, a dataset was produced consisting of spring locations as indicator and groundwater-conditioning factors (GCFs) as input. In this research, 13 GCFs were selected including altitude, slope aspect, slope angle, plan curvature, profile curvature, topographic wetness index (TWI), slope length, distance from rivers and faults, rivers and faults density, land use, and lithology. The mentioned dataset was divided into two classes of training and validation with 70 and 30% of the springs, respectively. Then, C5.0, RF, and MARS algorithms were employed using R statistical software, and the final values were transformed into GPMs. Finally, two evaluation criteria including Kappa and area under receiver operating characteristics curve (AUC-ROC) were calculated. According to the findings of this research, MARS had the best performance with AUC-ROC of 84.2%, followed by RF and C5.0 algorithms with AUC-ROC values of 79.7 and 77.3%, respectively. The results indicated that AUC-ROC values for the employed models are more than 70% which shows their acceptable performance. As a conclusion, the produced methodology could be used in other geographical areas. GPMs could be used by water resource managers and related organizations to accelerate and facilitate water resource exploitation.
The identification of spatio-temporal patterns of the urban growth phenomenon has become one of the most significant challenges in monitoring and assessing current and future trends of the urban growth issue. Therefore, spatio-temporal and quantitative techniques should be used hand in hand for a deeper understanding of various aspects of urban growth. The main purpose of this study is to monitor and assess the significant patterns of urban growth in Seremban using a spatio-temporal built-up area analysis. The concentric circles approach was used to measure the compactness and dispersion of built-up area by employing Shannon's Entropy method. The spatial directions approach was also utilised to measure the sustainability and speed of development, while the gradient approach was used to measure urban dynamics by employing landscape matrices. The overall results confirm that urban growth in Seremban is dispersed, unbalanced and unsustainable with a rapid speed of regional development. The main contribution of using existing methods with other methods is to provide several spatial and statistical dimensions that can help researchers, decision makers and local authorities understand the trend of growth and its patterns in order to take the appropriate decisions for future urban planning. For example, Shannon's Entropy findings indicate a high value of dispersion between the years 1990 and 2000 and from 2010 to 2016 with a growth rate of approximately 94 and 14%, respectively. Therefore, these results can help and support decision makers to implement alternative urban forms such as the compactness form to achieve an urban form that is more suitable and sustainable. The results of this study confirm the importance of using spatio-temporal built-up area and quantitative analysis to protect the sustainability of land use, as well as to improve the urban planning system via the effective monitoring and assessment of urban growth trends and patterns.