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.
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.
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.
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.
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
Synthetic dyes used in the textile and paper industries pose a major threat to the environment. In the present research work, the adsorption efficiency of the natural adsorbent Strychnos potatorum Linn (Fam: Loganiaceae) seeds were examined against the reactive orange-M2R dye from aqueous solution by varying the process conditions such as contact time, pH, adsorbent dosage, and initial dye concentration on adsorption of anionic azo dye. This study compares different types of artificial neural networks which are feedforward artificial neural network (FANN) and nonlinear autoregressive exogenous (NARX) model to predict the efficiency of a cost-effective natural adsorbent Strychnos potatorum Linn seeds on removing reactive orange-M2R dye from aqueous solution. Twelve training algorithms of neural network were compared, and the prediction on the adsorption performance of anionic azo dye from aqueous solution using Strychnos potatonum Linn seeds was evaluated by using the root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), and accuracy. For FANN model, Levenberg-Marquardt (LM) backpropagation with 19 hidden neurons was selected as the optimum FANN model, with R2 of 0.994 and accuracy of 87.20%, 98.21%, and 66.60% for training, testing, and validation datasets, respectively. For NARX model, LM with 8 hidden neurons was selected as the most suitable training algorithm, with R2 value of more than 0.99 and accuracy of 88.00%, 90.91%, and 75.00% for training, testing, and validation datasets, respectively. NARX model accurately predicted the adsorption of anionic azo dye from aqueous solution using Strychnos potatonum Linn seeds with better performance than FANN model.
We studied the water quality of the riparian firefly sanctuary of Sungai Rembau, or Rembau River, in Negeri Sembilan, Malaysia, from January 2018 to November 2018 to determine the possible influence of the physico-chemical characteristics of the water on the firefly populations living within the sanctuary. We set up a total of five water quality sampling stations and 10 firefly sampling stations along the river. Dissolved oxygen (DO), temperature, pH and electrical conductivity (EC) were measured in situ, while chemical oxygen demand (COD), total suspended solids (TSS), biochemical oxygen demand (BOD) and ammonia-nitrogen (NH3-N) were analysed in the laboratory. Firefly samples were collected using a sweep net at both day and night for 1 min. Sungai Rembau was categorized as Class II on the Malaysian water quality index (WQI), which indicates slight pollution. Except for EC and DO, the water quality parameter values were not significantly different (p > 0.05) between the sampling stations. A total of 529 firefly individuals consisting of Pteroptyx tener (n = 525, 99.24%), P. malaccae (n = 3, 0.57%) and P. asymmetria (n = 1, 0.19%) were collected. There was significant correlation between firefly abundance and BOD (r = - 0.198, p
Fluoride enrichment of groundwater has been adjudged to be a global environmental challenge in the past decade as most humans depend on groundwater for their domestic needs. This study was conducted to investigate the ionic and fluoride concentrations in borehole water and its associated health risk potentials to residents of Makurdi town and its environs, Benue state, Nigeria. Multivariate statistical techniques were for the first time used to explain the mechanisms of fluoride occurrence in groundwater in the study area. An aggregate of sixty-three (63) groundwater samples were retrieved from boreholes in twenty-one (21) diverse points within the study area and assessed for its physico-chemical composition with emphasis on fluoride content and health risk potentials following standard field and laboratory procedures. It was observed that fluoride content in the sampled water exceeded the stipulated safe limit of 1.5 mg/L in about 33.33% of the total samples and ranged from 0.34 to 2.06 mg/L with an average of 1.26 ± 0.41 mg/L. Moderate affirmative relationships were observed to exist between F- and TDS, F- and EC, F- and Cl-, and F- and NO3- in the water samples indicative of a common source pollution. Principal component analysis (PCA) revealed that high fluoride content in the water samples was associated with the dissolutions from quartzite and shale into the underlying deep aquifers as well as from contributions from anthropogenic activities including fertilizer and pesticide uses. Fluoride risk assessment indicated that the hazard quotient (HQ) for ingestion of fluoride laden water exceeded the threshold value in 66.7, 71.4, 52.4, and 9.5% of the samples for infants, children, teenagers, and adults respectively. It was found that multivariate statistical procedures such as PCA and correlation analysis (CA) are capable of establishing the relationship among groundwater pollutants, while hierarchical cluster analysis (HCA) was found suitable for explaining the likely sources/processes of pollutant enrichment in the groundwater. It is recommended that the findings of this study would serve as a basis for policy makers and regulatory bodies towards ameliorating the menace of groundwater contamination within the study area.
Rivers in Malaysia are classified based on water quality index (WQI) that comprises of six parameters, namely, ammoniacal nitrogen (AN), biochemical oxygen demand (BOD), chemical oxygen demand (COD), dissolved oxygen (DO), pH, and suspended solids (SS). Due to its tropical climate, the impact of seasonal monsoons on river quality is significant, with the increased occurrence of extreme precipitation events; however, there has been little discussion on the application of artificial intelligence models for monsoonal river classification. In light of these, this study had applied artificial neural network (ANN) and support vector machine (SVM) models for monsoonal (dry and wet seasons) river classification using three of the water quality parameters to minimise the cost of river monitoring and associated errors in WQI computation. A structured trial-and-error approach was applied on input parameter selection and hyperparameter optimisation for both models. Accuracy, sensitivity, and precision were selected as the performance criteria. For dry season, BOD-DO-pH was selected as the optimum input combination by both ANN and SVM models, with testing accuracy of 88.7% and 82.1%, respectively. As for wet season, the optimum input combinations of ANN and SVM models were BOD-pH-SS and BOD-DO-pH with testing accuracy of 89.5% and 88.0%, respectively. As a result, both optimised ANN and SVM models have proven their prediction capacities for river classification, which may be deployed as effective and reliable tools in tropical regions. Notably, better learning and higher capacity of the ANN model for dataset characteristics extraction generated better predictability and generalisability than SVM model under imbalanced dataset.
The massive destruction and loss caused by the 2004 Sumatra-Andaman tsunami were attributed to the lack of knowledge on tsunami and low regional detection and communication systems for early warning in that region. This study aimed to identify locations at risk of impending tsunami from Andaman Sea for the safety of community and proper development planning at the coastal areas by providing an updated and revised inundation maps. The last study on this area was conducted several years ago which open the possibility to new findings. Generated by tsunami simulation models, the maps illustrate the extent and level of inundation to which the coastal community and infrastructure would be subjected. As a result of coastal changes and availability of better topographic data, the existing inundation maps for the coastal areas of northwest Peninsular Malaysia at risk to impending tsunami from the Andaman Sea are revised. This paper documented the computational setup leading to the generation of the revised inundation maps. The tsunami simulation model TUNA was used to simulate the generation, propagation, and subsequent run-up and inundation of tsunamis triggered by earthquakes of moment magnitudes (Mw) 8.5, 9.0, and 9.25 along the Sunda Trench. From the simulations, it was found that at Mw 9.25, Balik Pulau, Pulau Pinang would be subjected to inundation of as far as 3.47 km with 5.40-m-deep inundation at the highest section.
The determinant factors for macroinvertebrate assemblages in river ecosystems are varied and are unique and specific to the type of macroinvertebrate family. This study aims to assess the determinant factors for macroinvertebrate assemblages in a recreational river. The study was conducted on the Ulu Bendul River, Negeri Sembilan, Malaysia. A total of ten sampling stations were selected. The research methodology included (1) water quality measurement, (2) habitat characterization, and (3) macroinvertebrate identification and distribution analysis. The statistical analysis used in this study was canonical correspondence analysis (CCA) to represent the relationship between the environmental factors and macroinvertebrate assemblages in the recreational river. This study found that most of the families of macroinvertebrates were very dependent on the temperature, DO, NH3-N, type of riverbed, etc. All of these factors are important for the survival of the particular type of macroinvertebrate, plus they are also important for selecting egg-laying areas and providing suitable conditions for the larvae to grow. This study advises that improved landscape design for watershed management be implemented in order to enhance water quality and physical habitats, and hence the protection and recovery of the macroinvertebrate biodiversity.
Stormwater runoff is a major concern in urban areas which is mostly the result of vast urbanization. To reduce urban stormwater runoff and improve water quality, low impact development (LID) is used in urban areas. Therefore, it is vital to find the optimal combination of LID controls to achieve maximum reduction in both stormwater runoff and pollutants with optimal cost. In this study, a simulation-optimization model was developed by linking the EPA Storm Water Management Model (SWMM) to the Multi-Objective Particle Swarm Optimization (MOPSO) using MATLAB. The coupled model could carry out multi-objective optimization (MOO) and find potential solutions to the optimization objectives using the SWMM simulation model outputs. The SWMM model was developed using data from the BUNUS catchment in Kuala Lumpur, Malaysia. The total suspended solids (TSS) and total nitrogen (TN) were selected as pollutants to be used in the simulation model. Vegetated swale and rain garden were selected as LID controls for the study area. The LID controls were assigned to the model using the catchment characteristics. The target objectives were to minimize peak stormwater runoff, TSS, and TN with the minimum number of LID controls applications. The LID combination scenarios were also tested in SWMM to identify the best LID types and combination to achieve maximum reduction in both peak runoff and pollutants. This study found that the peak runoff, TSS, and TN were reduced by 13%, 38%, and 24%, respectively. The optimal number of LID controls that could be used at the BUNUS catchment area was also found to be 25.
A widely distributed urban bird, the house crow (Corvus splendens), was used to assess bioavailable heavy metals in urban and rural environments across Pakistan. Bioaccumulation of arsenic (As), zinc (Zn), lead (Pb), cadmium (Cd), nickel (Ni), iron (Fe), manganese (Mn), chromium (Cr), and copper (Cu) was investigated in wing feathers of 96 crows collected from eight locations and categorized into four groups pertaining to their geographical and environmental similarities. Results revealed that the concentrations of Pb, Ni, Mn, Cu, and Cr were positively correlated and varied significantly among the four groups. Zn, Fe, Cr, and Cu regarded as industrial outputs, were observed in birds both in industrialized cities and in adjoining rural agricultural areas irrigated through the Indus Basin Irrigation System. Birds in both urban regions accrued Pb more than the metal toxicity thresholds for birds. The house crow was ranked in the middle on the metal accumulation levels in feathers between highly accumulating raptor and piscivore and less contaminated insectivore and granivore species in the studied areas,. This study suggests that the house crow is an efficient bioindicator and supports the feasibility of using feathers to discriminate the local pollution differences among terrestrial environments having different levels and kinds of anthropogenic activities.
Forests and agricultural lands are the main resources on the earth's surface and important indicators of regional ecological environments. In this paper, Landsat images from 1990 and 2017 were used to extract information on forests in Malaysia based on a remote-sensing classification method. The spatial-temporal changes of forests and agricultural lands in Malaysia between 1990 and 2017 were analyzed. The results showed that the natural forests in Malaysia decreased by 441 Mha, a reduction of 21%. The natural forests were mainly converted into plantations in Peninsular Malaysia and plantations and secondary forests in East Malaysia. The area of agricultural lands in Malaysia increased by 55.7%, in which paddy fields increased by 1.1% and plantations increased by 98.2%. Paddy fields in Peninsular Malaysia are mainly distributed in the north-central coast and the Kelantan Delta. The agricultural land in East Malaysia is dominated by plantations, which are mainly distributed in coastal areas. The predictable areas of possible expansion for paddy fields in Peninsular Malaysia's Kelantan (45.2%) and Kedah (16.8%) areas in the future are large, and in addition, the plantations in Sarawak (44.7%) and Sabah (29.6%) of East Malaysia have large areas for expansion. The contradiction between agricultural development and protecting the ecological environment is increasingly prominent. The demand for agriculture is expected to increase further and result in greater pressures on tropical forests. Governments also need to encourage farmers to carry out existing land development, land recultivation, or cooperative development to improve agricultural efficiency and reduce the damage to natural forests.
An environmental problem which is of concern across the globe nowadays is air pollution. The extent of air pollution is often studied based on data on the observed level of air pollution. Although the analysis of air pollution data that is available in the literature is numerous, studies on the dynamics of air pollution with the allowance for spatial interaction effects through the use of the Markov chain model are very limited. Accordingly, this study aims to explore the potential impact of spatial dependence over time and space on the distribution of air pollution based on the spatial Markov chain (SMC) model using the longitudinal air pollution index (API) data. This SMC model is pertinent to be applied since the daily data of API from 2012 to 2014 that have been gathered from 37 different air quality stations in Peninsular Malaysia is found to exhibit the property of spatial autocorrelation. Based on the spatial transition probability matrices found from the SMC model, specific characteristics of air pollution are studied in the regional context. These characteristics are the long-run proportion and the mean first passage time for each state of air pollution. It is found that the probability for a particular station's state to remain good is 0.814 if its neighbors are in a good state of air pollution and 0.7082 if its neighbors are in a moderate state. For a particular station having neighbors in a good state of air pollution, the proportion of time for it to continue being in a good state is 0.6. This proportion reduces to 0.4, 0.01, and 0 for the cell of moderate, unhealthy, and very unhealthy states, respectively. In addition, there exists a significant spatial dependence of API, indicating that air pollution for a particular station is dependent on the states of the neighboring stations.
We sampled the Klang estuary during the inter-monsoon and northeast monsoon period (July-Nov 2011, Oct-Nov 2012), which coincided with higher rainfall and elevated Klang River flow. The increased freshwater inflow into the estuary resulted in water column stratification that was observed during both sampling periods. Dissolved oxygen (DO) dropped below 63 μM, and hypoxia was observed. Elevated river flow also transported dissolved inorganic nutrients, chlorophyll a and bacteria to the estuary. However, bacterial production did not correlate with DO concentration in this study. As hypoxia was probably not due to in situ heterotrophic processes, deoxygenated waters were probably from upstream. We surmised this as DO correlated with salinity (R2 = 0.664, df = 86, p 6.7 h), hypoxia could occur at the Klang estuary. Here, we presented a model that related riverine flow rate to the post-heavy rainfall hypoxia that explicated the episodic hypoxia at Klang estuary. As Klang estuary supports aquaculture and cockle culture, our results could help protect the aquaculture and cockle culture industry here.
The assessment of surface water quality is often laborious, expensive and tedious, as well as impractical, especially for the developing and middle-income countries in the ASEAN region. The application of the water quality index (WQI), which depends on several independent key parameters, has great potential and is a useful tool in this region. Therefore, this study aims to find out the spatial variability of various water quality parameters in geographical information system (GIS) environment and perform a comparative study among the ASEAN WQI systems. At present, there are four ASEAN countries which have implemented the WQI system to evaluate their surface water quality, which are (i) Own WQI system-Malaysia, Thailand and Vietnam-and (ii) Adopted WQI system: Indonesia. A spatial distribution of 12 water quality parameters in the Selangor river basin, Malaysia, was plotted and then applied into the different ASEAN WQI systems. The WQI values obtained from the different WQI systems have an appreciable difference, even for the same water samples due to the disparity in the parameter selection and the standards among them. WQI systems which consider all biophysicochemical parameters provide a consistent evaluation (Very Poor), but the system which either considers physicochemical or biochemical parameters gives a relatively lenient evaluation (Fair-Poor). The Selangor river basin is stressed and impacted by all physical, biological and chemical parameters caused by both the aridity of the climate and anthropogenic activities. Therefore, it is crucial to include all these aspects into the evaluation and corresponding actions should be taken.
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.