Gaseous pollutants such as hydrogen gas (H2) are emitted in daily human activities. They have been massively studied owing to their high explosivity and widespread usage in many domains. The current research is designed to analyse optical fiber-based H2 gas sensors by incorporating palladium/graphene oxide (Pd/GO) nanocomposite coating as sensing layers. The fabricated multimode silica fiber (MMF) sensors were used as a transducing platform. The tapering process is essential to improve the sensitivity to the environment through the interaction of the evanescent field over the area of the tapered surface area. Several characterization methods including FESEM, EDX, AFM, and XRD were adopted to examine the structure properties of the materials and achieve more understandable facts about their functional performance of the optical sensor. Characterisation results demonstrated structures with a higher surface for analyte gas reaction to the optical sensor performance. Results indicated an observed increment in the Pd/GO nanocomposite-based sensor responses subjected to the H2 concentrations increased from 0.125% to 2.00%. The achieved sensitivities were 33.22/vol% with a response time of 48 s and recovery time of 7 min. The developed optical fiber sensors achieved excellent selectivity and stability toward H2 gas upon exposure to other gases such as ammonia and methane.
Global warming has led to sea levels raise (SLRs) and Malaysia is no exception to this problem. Especially for low-lying coastal areas including the Kuala Kedah area which is active in agricultural and fisheries activities. Farmers have had to bear up to 75 % of yield losses due to seawater breaches since 2016. Therefore, this study is designed to assess the impact of seawater encroachment on water quality through spatial technology approaches and hydrodynamic modeling related to the growth of paddy trees. The study was conducted during two different paddy cultivation seasons namely Season 1-2019 and Season 2-2019 which take place in the southwest and northeast monsoon in Kuala Kedah, Malaysia. The study involved three phases, which are the assessment of salinity and pH concentration levels, the assessment of the health of paddy crops through multispectral image analysis involving three plant indices (VI), namely Normalized Difference Vegetation Index (NDVI), Blue Normalized Difference Vegetation Index (BNDVI) and Normalized Difference Red Edge (NDRE), and finally, the assessment of the impact of SLR through the numerical method in MIKE 21 for hydrodynamic modeling considering two conditions that are without mitigation factor (K1) and with existing mitigating factor (K2). According to the findings, the salinity concentration trend is decreasing across the growth stage during Season 1-2019, whereas it is the contrary during Season 2-2019. It was discovered that during the study period for both tidal events, 73 % of the 44 sampling points in Season 1-2019, as opposed to just 3 % in Season 2-2019, were categorized as Class 4 and Class 5. Even though there were fluctuations throughout the observation, the pH reading is still within the allowed range of 6.5 to 9.0 for the estuary area. Following that, the ANOVA analysis proved that salinity concentration a statistically significant difference with tidal variations and pH levels. Moreover, the multispectral image analysis findings revealed that the VI value was correlated with both the yield and the health of the rice crop, with R-square values of 0.842 compared to 0.706 and 0.575 for NDVI and BNDVI values, respectively. It confirmed that NDRE granted a more accurate and reliable measurements. Additionally, the hydrodynamic simulation results demonstrated that, if the mitigation factors were considered in the modeling, overflow seawater to the mainland could be reduced by up to 20 %, reducing the impact of coastal flooding on the local area as well as the nearby rice cultivation area. Ultimately, these three elements-water quality, vegetation index, and hydrodynamic modeling-can assist in identifying the underlying cause of the problem and develop short and long-term solutions.
Heavy metals (HMs) are a vital elements for investigating the pollutant level of sediments and water bodies. The Murray-Darling river basin area located in Australia is experiencing severe damage to increased crop productivity, loss of soil fertility, and pollution levels within the vicinity of the river system. This basin is the most effective primary production area in Australia where agricultural productivity is increased the gross domastic product in the entire mainland. In this study, HMs contaminations are examined for eight study sites selected for the Murray-Darling river basin where the inverse Distance Weighting interpolation method is used to identify the distribution of HMs. To pursue this, four different pollution indices namely the Geo-accumulation index (Igeo), Contamination factor (CF), Pollution load index (PLI), single-factor pollution index (SPLI), and the heavy metal pollution index (HPI) are computed. Following this, the Pearson correlation matrix is used to identify the relationships among the two HM parameters. The results indicate that the conductivity and N (%) are relatively high in respect to using Igeo and PLI indexes for study sites 4, 6, and 7 with 2.93, 3.20, and 1.38, respectively. The average HPI is 216.9071 that also indicates higher level pollution in the Murray-Darling river basin and the highest HPI value is noted in sample site 1 (353.5817). The study also shows that the levels of Co, P, Conductivity, Al, and Mn are mostly affected by HMs and that these indices indicate the maximum HM pollution level in the Murray-Darling river basin. Finally, the results show that the high HM contamination level appears to influence human health and local environmental conditions.
Urban areas are quickly established, and the overwhelming population pressure is triggering heat stress in the metropolitan cities. Climate change impact is the key aspect for maintaining the urban areas and building proper urban planning because spreading of the urban area destroyed the vegetated land and increased heat variation. Remote sensing-based on Landsat images are used for investigating the vegetation circumstances, thermal variation, urban expansion, and surface urban heat island or SUHI in the three megacities of Iraq like Baghdad, Erbil, and Basrah. Four satellite imageries are used aimed at land use and land cover (LULC) study from 1990 to 2020, which indicate the land transformation of those three major cities in Iraq. The average annually temperature is increased during 30 years like Baghdad (0.16 °C), Basrah (0.44 °C), and Erbil (0.32 °C). The built-up area is increased 147.1 km2 (Erbil), 217.86 km2 (Baghdad), and 294.43 km2 (Erbil), which indicated the SUHI affects the entire area of the three cities. The bare land is increased in Baghdad city, which indicated the local climatic condition and affected the livelihood. Basrah City is affected by anthropogenic activities and most areas of Basrah were converted into built-up land in the last 30 years. In Erbil, agricultural land (295.81 km2) is increased. The SUHI study results indicated the climate change effect in those three cities in Iraq. This study's results are more useful for planning, management, and sustainable development of urban areas.
This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM2.5 forecasting maps.
Climatic condition is triggering human health emergencies and earth's surface changes. Anthropogenic activities, such as built-up expansion, transportation development, industrial works, and some extreme phases, are the main reason for climate change and global warming. Air pollutants are increased gradually due to anthropogenic activities and triggering the earth's health. Nitrogen Dioxide (NO2), Carbon Monoxide (CO), and Aerosol Optical Depth (AOD) are truthfully important for air quality measurement because those air pollutants are more harmful to the environment and human's health. Earth observational Sentinel-5P is applied for monitoring the air pollutant and chemical conditions in the atmosphere from 2018 to 2021. The cloud computing-based Google Earth Engine (GEE) platform is applied for monitoring those air pollutants and chemical components in the atmosphere. The NO2 variation indicates high during the time because of the anthropogenic activities. Carbon Monoxide (CO) is also located high between two 1-month different maps. The 2020 and 2021 results indicate AQI change is high where 2018 and 2019 indicates low AQI throughout the year. The Kolkata have seven AQI monitoring station where high nitrogen dioxide recorded 102 (2018), 48 (2019), 26 (2020) and 98 (2021), where Delhi AQI stations recorded 99 (2018), 49 (2019), 37 (2020), and 107 (2021). Delhi, Kolkata, Mumbai, Pune, and Chennai recorded huge fluctuations of air pollutants during the study periods, where ~ 50-60% NO2 was recorded as high in the recent time. The AOD was noticed high in Uttar Pradesh in 2020. These results indicate that air pollutant investigation is much necessary for future planning and management otherwise; our planet earth is mostly affected by the anthropogenic and climatic conditions where maybe life does not exist.
According to recent reports, planar structure-based organometallic perovskite solar cells (OPSCs) have achieved remarkable power conversion efficiency (PCE), making them very competitive with the more traditional silicon photovoltaics. A complete understanding of OPSCs and their individual parts is still necessary for further enhancement in PCE. In this work, indium sulfide (In2S3)-based planar heterojunction OPSCs were proposed and simulated with the SCAPS (a Solar Cell Capacitance Simulator)-1D programme. Initially, OPSC performance was calibrated with the experimentally fabricated architecture (FTO/In2S3/MAPbI3/Spiro-OMeTAD/Au) to evaluate the optimum parameters of each layer. The numerical calculations showed a significant dependence of PCE on the thickness and defect density of the MAPbI3 absorber material. The results showed that as the perovskite layer thickness increased, the PCE improved gradually but subsequently reached a maximum at thicknesses greater than 500 nm. Moreover, parameters involving the series resistance as well as the shunt resistance were recognized to affect the performance of the OPSC. Most importantly, a champion PCE of over 20% was yielded under the optimistic simulation conditions. Overall, the OPSC performed better between 20 and 30 °C, and its efficiency rapidly decreases above that temperature.
The COVID-19 pandemic was a serious global health emergency in 2020 and 2021. This study analyzed the seasonal association of weekly averages of meteorological parameters, such as wind speed, solar radiation, temperature, relative humidity, and air pollutant PM2.5, with confirmed COVID-19 cases and deaths in Baghdad, Iraq, a major megacity of the Middle East, for the period June 2020 to August 2021. Spearman and Kendall correlation coefficients were used to investigate the association. The results showed that wind speed, air temperature, and solar radiation have positive and strong correlations with the confirmed cases and deaths in the cold season (autumn and winter 2020-2021). The total COVID-19 cases negatively correlated with relative humidity but were not significant in all seasons. Besides, PM2.5 strongly correlated with COVID-19 confirmed cases for the summer of 2020. The death distribution by age group showed the highest deaths for those aged 60-69. The highest number of deaths was 41% in the summer of 2020. The study provided useful information about the COVID-19 health emergency and meteorological parameters, which can be used for future health disaster planning, adopting prevention strategies and providing healthcare procedures to protect against future infraction transmission.
Developments in the transportation field are emerging because of the growing worldwide demand and upgrading requirements. This study measured the transportation development, shortage distance, and decadal land transformation of Kuala Lumpur and Madrid using various remote sensing and GIS approaches. The kernel density estimation (KDE) tool was applied for road and railway density analysis, and hotspot information increased the knowledge about assessable areas. Landsat datasets were used (1991-2021) for land transformation and related analyses. The built-up land increased by 1327.27 and 404.09 km2 in Kuala Lumpur and Madrid, respectively. In the last thirty years, the temperature increased 6.45 °C in Kuala Lumpur and 4.15 °C in Madrid owing to urban expansion and road construction. Chamberi, Retiro, Moratalaz, Salama, Wangsa Maju, Titiwangsa, Bukit Bintang, and Seputeh have very high road densities. KDE measurements showed that the road densities in Kuala Lumpur (4498.34) and Madrid (9099.15) were high in the central parts of the city, and the railway densities were 348.872 and 2197.87, respectively. The observed P values were 0.99 and 0.96 for traffic signals and 0.98 and 0.99 for bus stops, respectively. The information provided by this study can support local planners, administrators, scientists, and researchers in understanding the global transportation issues that require implementation strategies for ensuring sustainable livelihoods.
The Great Lakes are critical freshwater sources, supporting millions of people, agriculture, and ecosystems. However, climate change has worsened droughts, leading to significant economic and social consequences. Accurate multi-month drought forecasting is, therefore, essential for effective water management and mitigating these impacts. This study introduces the Multivariate Standardized Lake Water Level Index (MSWI), a modified drought index that utilizes water level data collected from 1920 to 2020. Four hybrid models are developed: Support Vector Regression with Beluga whale optimization (SVR-BWO), Random Forest with Beluga whale optimization (RF-BWO), Extreme Learning Machine with Beluga whale optimization (ELM-BWO), and Regularized ELM with Beluga whale optimization (RELM-BWO). The models forecast droughts up to six months ahead for Lake Superior and Lake Michigan-Huron. The best-performing model is then selected to forecast droughts for the remaining three lakes, which have not experienced severe droughts in the past 50 years. The results show that incorporating the BWO improves the accuracy of all classical models, particularly in forecasting drought turning and critical points. Among the hybrid models, the RELM-BWO model achieves the highest level of accuracy, surpassing both classical and hybrid models by a significant margin (7.21 to 76.74%). Furthermore, Monte-Carlo simulation is employed to analyze uncertainties and ensure the reliability of the forecasts. Accordingly, the RELM-BWO model reliably forecasts droughts for all lakes, with a lead time ranging from 2 to 6 months. The study's findings offer valuable insights for policymakers, water managers, and other stakeholders to better prepare drought mitigation strategies.
Drought is a hazardous natural disaster that can negatively affect the environment, water resources, agriculture, and the economy. Precise drought forecasting and trend assessment are essential for water management to reduce the detrimental effects of drought. However, some existing drought modeling techniques have limitations that hinder precise forecasting, necessitating the exploration of suitable approaches. This study examines two forecasting models, Long Short-Term Memory (LSTM) and a hybrid model integrating regularized extreme learning machine and Snake algorithm, to forecast hydrological droughts for one to six months in advance. Using the Multivariate Standardized Streamflow Index (MSSI) computed from 58 years of streamflow data for two drier Malaysian stations, the models forecast droughts and were compared to classical models such as gradient boosting regression and K-nearest model for validation purposes. The RELM-SO model outperformed other models for forecasting one month ahead at station S1, with lower root mean square error (RMSE = 0.1453), mean absolute error (MAE = 0.1164), and a higher Nash-Sutcliffe efficiency index (NSE = 0.9012) and Willmott index (WI = 0.9966). Similarly, at station S2, the hybrid model had lower (RMSE = 0.1211 and MAE = 0.0909), and higher (NSE = 0.8941 and WI = 0.9960), indicating improved accuracy compared to comparable models. Due to significant autocorrelation in the drought data, traditional statistical metrics may be inadequate for selecting the optimal model. Therefore, this study introduced a novel parameter to evaluate the model's effectiveness in accurately capturing the turning points in the data. Accordingly, the hybrid model significantly improved forecast accuracy from 19.32 % to 21.52 % when compared with LSTM. Besides, the reliability analysis showed that the hybrid model was the most accurate for providing long-term forecasts. Additionally, innovative trend analysis, an effective method, was used to analyze hydrological drought trends. The study revealed that October, November, and December experienced higher occurrences of drought than other months. This research advances accurate drought forecasting and trend assessment, providing valuable insights for water management and decision-making in drought-prone regions.
This study proposes different standalone models viz: Elman neural network (ENN), Boosted Tree algorithm (BTA), and f relevance vector machine (RVM) for modeling arsenic (As (mg/kg)) and zinc (Zn (mg/kg)) in marine sediments owing to anthropogenic activities. A heuristic algorithm based on the potential of RVM and a flower pollination algorithm (RVM-FPA) was developed to improve the prediction performance. Several evaluation indicators and graphical methods coupled with visualized cumulative probability function (CDF) were used to evaluate the accuracy of the models. Akaike (AIC) and Schwarz (SCI) information criteria based on Dickey-Fuller (ADF) and Philip Perron (PP) tests were introduced to check the reliability and stationarity of the data. The prediction performance in the verification phase indicated that RVM-M2 (PBAIS = -o.0465, MAE = 0.0335) and ENN-M2 (PBAIS = 0.0043, MAE = 0.0322) emerged as the best model for As (mg/kg) and Zn (mg/kg), respectively. In contrast with the standalone approaches, the simulated hybrid RVM-FPA proved merit and the most reliable, with a 5 % and 18 % predictive increase for As (mg/kg) and Zn (mg/kg), respectively. The study's findings validated the potential for estimating complex HMs through intelligent data-driven models and heuristic optimization. The study also generated valuable insights that can inform the decision-makers and stockholders for environmental management strategies.
A composite of chitosan biopolymer with microalgae and commercial carbon-doped titanium dioxide (kronos) was modified by grafting an aromatic aldehyde (salicylaldehyde) in a hydrothermal process for the removal of brilliant green (BG) dye. The resulting Schiff's base Chitosan-Microalgae-TiO2 kronos/Salicylaldehyde (CsMaTk/S) material was characterised using various analytical methods (conclusive of physical properties using BET surface analysis method, elemental analysis, FTIR, SEM-EDX, XRD, XPS and point of zero charge). Box Behnken Design was utilised for the optimisation of the three input variables, i.e., adsorbent dose, pH of the media and contact time. The optimum conditions appointed by the optimisation process were further affirmed by the desirability test and employed in the equilibrium studies in batch mode and the results exhibited a better fit towards the pseudo-second-order kinetic model as well as Freundlich and Langmuir isotherm models, with a maximum adsorption capacity of 957.0 mg/g. Furthermore, the reusability study displayed the adsorptive performance of CsMaTk/S remains effective throughout five adsorption cycles. The possible interactions between the dye molecules and the surface of the adsorbent were derived based on the analyses performed and the electrostatic attractions, H-bonding, Yoshida-H bonding, π-π and n-π interactions are concluded to be the responsible forces in this adsorption process.
Global warming induces spatially heterogeneous changes in precipitation patterns, highlighting the need to assess these changes at regional scales. This assessment is particularly critical for Afghanistan, where agriculture serves as the primary livelihood for the population. New global climate model (GCM) simulations have recently been released for the recently established shared socioeconomic pathways (SSPs). This requires evaluating projected precipitation changes under these new scenarios and subsequent policy updates. This research employed six GCMs from the CMIP6 to project spatial and temporal precipitation changes across Afghanistan under all SSPs, including SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5. The employed GCMs were bias-corrected using the Global Precipitation Climatological Center's (GPCC) monthly gridded precipitation data with a 1.0° spatial resolution. Subsequently, the climate change factor was calculated to assess precipitation changes for both the near future (2020-2059) and the distant future (2060-2099). The bias-corrected projections' multi-model ensemble (MME) revealed increased precipitation across most of Afghanistan for SSPs with higher emissions scenarios. The bias-corrected simulations showed a substantial increase in summer precipitation of around 50%, projected under SSP1-1.9 in the southwestern region, while a decline of over 50% is projected in the northwestern region until 2100. The annual precipitation in the northwest region was projected to increase up to 15% for SSP1-2.6. SSP2-4.5 showed a projected annual precipitation increase of around 20% in the southwestern and certain eastern regions in the far future. Furthermore, a substantial rise of approximately 50% in summer precipitation under SSP3-7.0 is expected in the central and western regions in the far future. However, it is crucial to note that the projected changes exhibit considerable uncertainty among different GCMs.
River water quality management and monitoring are essential responsibilities for communities near rivers. Government decision-makers should monitor important quality factors like temperature, dissolved oxygen (DO), pH, and biochemical oxygen demand (BOD). Among water quality parameters, the BOD throughout 5 days is an important index that must be detected by devoting a significant amount of time and effort, which is a source of significant concern in both academic and commercial settings. The traditional experimental and statistical methods cannot give enough accuracy or solve the problem for a long time to detect something. This study used a unique hybrid model called MVMD-LWLR, which introduced an innovative method for forecasting BOD in the Klang River, Malaysia. The hybrid model combines a locally weighted linear regression (LWLR) model with a wavelet-based kernel function, along with multivariate variational mode decomposition (MVMD) for the decomposition of input variables. In addition, categorical boosting (Catboost) feature selection was used to discover and extract significant input variables. This combination of MVMD-LWLR and Catboost is the first use of such a complete model for predicting BOD levels in the given river environment. In addition, an optimization process was used to improve the performance of the model. This process utilized the gradient-based optimization (GBO) approach to fine-tune the parameters and better the overall accuracy of predicting BOD levels. To assess the robustness of the proposed method, we compared it to other popular models such as kernel ridge (KRidge) regression, LASSO, elastic net, and gaussian process regression (GPR). Several metrics, comprising root-mean-square error (RMSE), R (correlation coefficient), U95% (uncertainty coefficient at 95% level), and NSE (Nash-Sutcliffe efficiency), as well as visual interpretation, were used to evaluate the predictive efficacy of hybrid models. Extensive testing revealed that, in forecasting the BOD parameter, the MVMD-LWLR model outperformed its competitors. Consequently, for BOD forecasting, the suggested MVMD-LWLR optimized with the GBO algorithm yields encouraging and reliable results, with increased forecasting accuracy and minimal error.
This research was established to accurately forecast daily scale air quality index (AQI) which is an essential environmental index for decision-making. Researchers have projected different types of models and methodologies for AQI forecasting, such as statistical techniques, machine learning (ML), and most recently deep learning (DL) models. The modelling development was adopted for Delhi city, India which is a major city with air pollution issues simialir to entire urban cities of India especially during winter seasons. This research was predicted AQI using different versions of DL models including Long-Short Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM) and Bidirectional Recurrent Neural Networks (Bi-RNN) in addition to Kernel Ridge Regression (KRR). Results indicated that Bi-RNN model consistently outperformed the other models in both training and testing phases, while the KRR model consistently displayed the weakest performance. The outstanding performance of the models development displayed the requirement of adequate data to train the models. The outcomes of the models showed that LSTM, BI-LSTM, KRR had lower performance compared with Bi-RNN models. Statistically, Bi-RNN model attained maximum determination coefficient (R2 = 0.954) and minimum root mean square error (RMSE = 25.755). The proposed model in this research revealed the robust predictable to provide a valuable base for decision-making in the expansion of combined air pollution anticipation and control policies targeted at addressing composite air pollution problems in the Delhi city.
Elevated usage of pharmaceutical products leads to the accumulation of emerging contaminants in sewage. In the current work, Ganoderma lucidum (GL) was used to remove pharmaceutical compounds (PCs), proposed as a tertiary method in sewage treatment plants (STPs). The PCs consisted of a group of painkillers (ketoprofen, diclofenac, and dexamethasone), psychiatrists (carbamazepine, venlafaxine, and citalopram), beta-blockers (atenolol, metoprolol, and propranolol), and anti-hypertensives (losartan and valsartan). The performance of 800 mL of synthetic water, effluent STP, and hospital wastewater (HWW) was evaluated. Parameters, including treatment time, inoculum volume, and mechanical agitation speed, have been tested. The toxicity of the GL after treatment is being studied based on exposure levels to zebrafish embryos (ZFET) and the morphology of the GL has been observed via Field Emission Scanning Electron Microscopy (FESEM). The findings conclude that GL can reduce PCs from <10% to >90%. Diclofenac and valsartan are the highest (>90%) in the synthetic model, while citalopram and propranolol (>80%) are in the real wastewater. GL effectively removed pollutants in 48 hours, 1% of the inoculum volume, and 50 rpm. The ZFET showed GL is non-toxic (LC50 is 209.95 mg/mL). In the morphology observation, pellets GL do not show major differences after treatment, showing potential to be used for a longer treatment time and to be re-usable in the system. GL offers advantages to removing PCs in water due to their non-specific extracellular enzymes that allow for the biodegradation of PCs and indicates a good potential in real-world applications as a favourable alternative treatment.