The enhancement of up-flow constructed wetland-microbial fuel cell (UFCW-MFC) performance in energy retrieval from caffeine containing wastewater has been explored via various operating conditions (hydraulic retention time (HRT), multianode (MA), multicathode current collector (MC), external resistance). The anaerobic decaffeination and COD removal improved by 37 and 12% as the HRT extended from 1 to 5 d. The increment in contact time between the microbes and organic substrates promoted the degradation and contributed to higher power output (3.4-fold), CE (eightfold), and NER (14-16-fold). The MA and MC connections facilitated the electron transfer rate and the degradation rate of organic substrates in the multiple anodic zones, which enhanced the removal efficiency in the anaerobic compartment (Caffeine: 4.2%; COD: 7.4%) and led to higher electricity generation (Power: 4.7-fold) and energy recovery (CE: 1.4-fold; NER: 2.3-2.5-fold) compared to SA. The lower external resistance favored the growth of electrogens and induced higher electron flux, where the best treatment performance and electricity production was obtained when the external resistance approached the internal resistance. Overall, it was noteworthy that the optimum operating conditions were achieved with 5 d HRT, MA, and MC connection along with external resistance of 200 Ω, which significantly outperformed the initial conditions (1 d HRT, SA connection, and 1000 Ω) by 43.7 and 29.8% of caffeine and COD removal in the anaerobic compartment, respectively as well as 14-fold of power generation.
Rapid urbanization has caused severe deterioration of air quality globally, leading to increased hospitalization and premature deaths. Therefore, accurate prediction of air quality is crucial for mitigation planning to support urban sustainability and resilience. Although some studies have predicted air pollutants such as particulate matter (PM) using machine learning algorithms (MLAs), there is a paucity of studies on spatial hazard assessment with respect to the air quality index (AQI). Incorporating PM in AQI studies is crucial because of its easily inhalable micro-size which has adverse impacts on ecology, environment, and human health. Accurate and timely prediction of the air quality index can ensure adequate intervention to aid air quality management. Therefore, this study undertakes a spatial hazard assessment of the air quality index using particulate matter with a diameter of 10 μm or lesser (PM10) in Selangor, Malaysia, by developing four machine learning models: eXtreme Gradient Boosting (XGBoost), random forest (RF), K-nearest neighbour (KNN), and Naive Bayes (NB). Spatially processed data such as NDVI, SAVI, BU, LST, Ws, slope, elevation, and road density was used for the modelling. The model was trained with 70% of the dataset, while 30% was used for cross-validation. Results showed that XGBoost has the highest overall accuracy and precision of 0.989 and 0.995, followed by random forest (0.989, 0.993), K-nearest neighbour (0.987, 0.984), and Naive Bayes (0.917, 0.922), respectively. The spatial air quality maps were generated by integrating the geographical information system (GIS) with the four MLAs, which correlated with Malaysia's air pollution index. The maps indicate that air quality in Selangor is satisfactory and posed no threats to health. Nevertheless, the two algorithms with the best performance (XGBoost and RF) indicate that a high percentage of the air quality is moderate. The study concludes that successful air pollution management policies such as green infrastructure practice, improvement of energy efficiency, and restrictions on heavy-duty vehicles can be adopted in Selangor and other Southeast Asian cities to prevent deterioration of air quality in the future.
Red mud as industrial waste from bauxite was utilized as a precursor for the synthesis of mesoporous ZSM-5. A high concentration of iron oxide in red mud was successfully removed using alkali fusion treatment. Mesoporous ZSM-5 was synthesized using cetyltrimethylammonium bromide (CTABr) as a template via dual-hydrothermal method, and the effect of crystallization time was investigated towards the formation of mesopores. Characterization using X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), N2 adsorption-desorption, scanning electron microscopy (SEM), and transmission electron microscopy (TEM) indicated the formation of cubic crystallite ZSM-5 with high surface area and mesopore volume within 6 h of crystallization. Increasing the crystallization time revealed the evolution of highly crystalline ZSM-5; however, the surface area and mesoporosity were significantly reduced. The effect of mesoporosity was investigated on the adsorption of methylene blue (MB). Kinetic and thermodynamic analysis of MB adsorption on mesoporous ZSM-5 was carried out at a variation of adsorption parameters such as the concentration of MB solution, the temperatures of solution, and the amount of adsorbent. Finally, methanol, 1-butanol, acetone, hydrochloric acid (HCl), and acetonitrile were used as desorbing agents to investigate the reusability and stability of mesoporous ZSM-5 as an adsorbent for MB removal.
This study examined (1) the effect of environmental concern, environmental knowledge, and health consciousness on attitude towards eBikes; (2) the effect of eBikes attributes of interest and infrastructure on perceived behavioural control; and finally, (3) the effect of attitude towards eBikes, subjective norms, and perceived behavioural control on eBikes commuting intention and its usage among Malaysian youth. This study adopted a cross-sectional design and convenience sampling, and collected quantitative data from 699 Malaysian youth through an online survey. Findings revealed that (1) environmental concern, environmental knowledge, and health consciousness had a positive and significant impact on attitude towards eBikes; (2) eBikes attributes of interest and infrastructure were positively and significantly related to perceived behavioural control; (3) attitude towards eBikes, subjective norms, and perceived behavioural control had a positive and significant effect on eBikes commuting intention; (4) eBikes commuting intention was positively and significantly related to the usage of eBikes; (5) eBikes commuting intention significantly mediated the relationship between attitude towards eBikes and perceived behavioural control on the usage of eBikes among Malaysian youth; and (6) eBikes commuting intention did not mediate the relationship between social norm and usage of eBikes. Although environmental knowledge and social norm are proven to have a positive and significant relationship, this analysis demonstrated a relatively low effect size. To promote environmental and sustainable development in cities through the mass adoption of eBikes among Malaysian youth, policymakers should highlight the benefits of using eBikes, introduce proper policies, and involve the development of improved and connected cycling paths in the city sustainable infrastructure plans.
This study investigated the degradation pathway of metoprolol, a widely used β-blocker, in the ozonation via the identification of generated ozonation by-products (OPs). Structure elucidation of OPs was performed using HPLC coupled with quadrupole time-of-flight high-resolution mass spectrometry. Seven OPs were identified, and four of these have not been reported elsewhere. Identified OPs of metoprolol included aromatic ring breakdown by-products; aliphatic chain degraded by-products and aromatic ring mono-, di-, and tetrahydroxylated derivatives. Based on the detected OPs, metoprolol could be degraded through aromatic ring opening reaction via reaction with ozone (O3) and degradation of aliphatic chain and aromatic ring via reaction with hydroxyl radical (•OH).
Dioxin-like compounds (DLCs) have been classified by the World Health Organization (WHO) as one of the most persistent toxic chemical substances in the environment, and they are associated with several occupational activities and industrial accidents around the world. Since the end of the 1970s, these toxic chemicals have been banned because of their human toxicity potential, long half-life, wide dispersion, and they bioaccumulate in the food web. This review serves as a primer for environmental health professionals to provide guidance on short-term risk assessment of dioxin and to identify key findings for health and exposure assessment based on policies of different agencies. It also presents possible health effects of dioxins, mechanisms of action, toxic equivalency factors (TEFs), and dose-response characterization. Key studies related to toxicity values of dioxin-like compounds and their possible human health risk were identified through PubMed and supplemented with relevant studies characterized by reviewing the reference lists in the review articles and primary literature. Existing data decreases the scope of analyses and models in relevant studies to a manageable size by focusing on the set of important studies related to the perspective of developing toxicity values of DLCs.
The basic aim of this work is (1) to review and present practically operational requirements for a sustainability assessment of marine environment, such as describing the monitoring process, research approaches, objectives, guidelines, and indicators and (2) to illustrate how physico-chemical and biological indicators can be practically applied, to assess water and sediment quality in marine and coastal environment. These indicators should meet defined criteria for practical usefulness, e.g. they should be simple to understand and apply to managers and scientists with different educational backgrounds. This review aimed to encapsulate that variability, recognizing that meaningful guidance should be flexible enough to accommodate the widely differing characteristics of marine ecosystems.
Based on the natural resource-based view theory, firms with improved environmental performance will have higher financial performance. Conversely, the neo-classical economic theory argues that improved environmental performance leads to lower financial performance. We argue that these contradicting findings are due to the political connection and treat it as the moderating variable to strengthen the positive effect of environmental performance on financial performance. Using carbon emission, spill volume, and total environmental fines as the environmental performance proxy, we only find that spill volume has a positive relationship with oil and gas companies' financial performance, supporting the natural resource-based view theory. Our results also show the positive effect of political connection on financial performance, supporting the helping-hand theory. Meanwhile, the moderating effect of political connection is only significant in weakening the negative impact of spill volume on financial performance. Practically, government regulation that bans the involvement of politicians in the boards of the oil and gas companies may allow firms to internalize the cost of the environmental damage, and could improve the environmental performance of the firms.
Rapid evolution in the population age structure of the Middle East countries has major economic, social, and environmental outcomes. Therefore, to fill the gap in the previous literatures, in this study, the effect of age structure on environmental degradation was investigated in the Middle East region. To achieve this goal, a panel data of 10 Middle East countries were examined over the period of 1990 to 2014. Moreover, the carbon dioxide emission per capita was used as an environmental pollution index in this study. According to the stationary property of the variables, small sample size data, and the assumptions of the model, the panel autoregressive distributed lag method of mean group, pooled mean group, and dynamic fixed effect estimators were investigated in this study. The empirical results implied that the pooled mean group model emerged as the most efficient among the three estimators. Also, results revealed that the age structure have a significant relationship with environmental pollution. Children and working age population have a positive elasticity, whereas elderly people have negative elasticity. Furthermore, the results showed that the working age population has the greatest explanatory power on the carbon emissions. Also, the relationship between per capita energy consumption and gross domestic product per capita with air pollution was positive. Overall, the empirical results showed that any attempt to decrease carbon dioxide emissions in the Middle East region should consider the population age structure.
In the present work, we prepared MgO-La2O3-mixed-metal oxides (MMO) as efficient photocatalysts for degradation of organic pollutants. First, a series of MgAl-%La-CO3-layered double hydroxide (LDH) precursors with different contents of La (5, 10, and 20 wt%) were synthesized by the co-precipitation process and then calcined at 600 °C. The prepared materials were characterized by XRD, SEM-EDX, FTIR, TGA, ICP, and UV-vis diffuse reflectance spectroscopy. XRD indicated that MgO, La2O3, and MgAl2O4 phases were found to coexist in the calcined materials. Also, XRD confirms the orthorhombic-tetragonal phases of MgO-La2O3. The samples exhibited a small band gap of 3.0-3.22 eV based on DRS. The photocatalytic activity of the catalysts was assessed for the degradation of two dyes, namely, tartrazine (TZ) and patent blue (PB) as model organic pollutants in aqueous mediums under UV-visible light. Detailed photocatalytic tests that focused on the impacts of dopant amount of La, catalyst dose, initial pH of the solution, irradiation time, dye concentration, and reuse were carried out and discussed in this research. The experimental findings reveal that the highest photocatalytic activity was achieved with the MgO-La2O3-10% MMO with photocatalysts with a degradation efficiency of 97.4% and 93.87% for TZ and PB, respectively, within 150 min of irradiation. The addition of La to the sample was responsible for its highest photocatalytic activity. Response surface methodology (RSM) and gradient boosting regressor (GBR), as artificial intelligence techniques, were employed to assess individual and interactive influences of initial dye concentration, catalyst dose, initial pH, and irradiation time on the degradation performance. The GBR technique predicts the degradation efficiency results with R2 = 0.98 for both TZ and PB. Moreover, ANOVA analysis employing CCD-RSM reveals a high agreement between the quadratic model predictions and the experimental results for TZ and PB (R2 = 0.9327 and Adj-R2 = 0.8699, R2 = 0.9574 and Adj-R2 = 0.8704, respectively). Optimization outcomes indicated that maximum degradation efficiency was attained under the following optimum conditions: catalyst dose 0.3 g/L, initial dye concentration 20 mg/L, pH 4, and reaction time 150 min. On the whole, this study confirms that the proposed artificial intelligence (AI) techniques constituted reliable and robust computer techniques for monitoring and modeling the photodegradation of organic pollutants from aqueous mediums by MgO-La2O3-MMO heterostructure catalysts.
Surface and ground water resources are highly sensitive aquatic systems to contaminants due to their accessibility to multiple-point and non-point sources of pollutions. Determination of water quality variables using mathematical models instead of laboratory experiments can have venerable significance in term of the environmental prospective. In this research, application of a new developed hybrid response surface method (HRSM) which is a modified model of the existing response surface model (RSM) is proposed for the first time to predict biochemical oxygen demand (BOD) and dissolved oxygen (DO) in Euphrates River, Iraq. The model was constructed using various physical and chemical variables including water temperature (T), turbidity, power of hydrogen (pH), electrical conductivity (EC), alkalinity, calcium (Ca), chemical oxygen demand (COD), sulfate (SO4), total dissolved solids (TDS), and total suspended solids (TSS) as input attributes. The monthly water quality sampling data for the period 2004-2013 was considered for structuring the input-output pattern required for the development of the models. An advance analysis was conducted to comprehend the correlation between the predictors and predictand. The prediction performances of HRSM were compared with that of support vector regression (SVR) model which is one of the most predominate applied machine learning approaches of the state-of-the-art for water quality prediction. The results indicated a very optimistic modeling accuracy of the proposed HRSM model to predict BOD and DO. Furthermore, the results showed a robust alternative mathematical model for determining water quality particularly in a data scarce region like Iraq.
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.
Choosing a suitable gridded climate dataset is a significant challenge in hydro-climatic research, particularly in areas lacking long-term, reliable, and dense records. This study used the most common method (Perkins skill score (PSS)) with two advanced time series similarity algorithms, short time series distance (STS), and cross-correlation distance (CCD), for the first time to evaluate, compare, and rank five gridded climate datasets, namely, Climate Research Unit (CRU), TERRA Climate (TERRA), Climate Prediction Center (CPC), European Reanalysis V.5 (ERA5), and Climatologies at high resolution for Earth's land surface areas (CHELSA), according to their ability to replicate the in situ rainfall and temperature data in Nigeria. The performance of the methods was evaluated by comparing the ranking obtained using compromise programming (CP) based on four statistical criteria in replicating in situ rainfall, maximum temperature, and minimum temperature at 26 locations distributed over Nigeria. Both methods identified CRU as Nigeria's best-gridded climate dataset, followed by CHELSA, TERRA, ERA5, and CPC. The integrated STS values using the group decision-making method for CRU rainfall, maximum and minimum temperatures were 17, 10.1, and 20.8, respectively, while CDD values for those variables were 17.7, 11, and 12.2, respectively. The CP based on conventional statistical metrics supported the results obtained using STS and CCD. CRU's Pbias was between 0.5 and 1; KGE ranged from 0.5 to 0.9; NSE ranged from 0.3 to 0.8; and NRMSE between - 30 and 68.2, which were much better than the other products. The findings establish STS and CCD's ability to evaluate the performance of climate data by avoiding the complex and time-consuming multi-criteria decision algorithms based on multiple statistical metrics.
Sustainable crowdfunding has emerged as a significant factor in the quest for alternative funding streams in recent times. The process has entailed the removal of financial obstacles and intermediaries, facilitating proximity between entrepreneurs' initiatives and fund providers, thereby initiating modifications in conventional investment and profitability criteria. The correlation between corporate social responsibility (CSR) and sustainable business returns is a significant metric that may enhance funding costs. CSR initiatives and crowdfunding possess the potential for mutually beneficial outcomes in terms of fundraising. However, fundraisers encounter obstacles and competition in their efforts to attain their donation objectives. As an illustration, CSR endeavors may provide a chance to raise capital via crowdfunding. Conversely, crowdfunding has the potential to serve as a means of micro-funding various social initiatives that align with a corporation's corporate social responsibility objectives. The present research investigates the correlation between efficacious donation fundraising campaigns in the context of crowdfunding endeavors that hold the possibility of transforming into corporate social responsibility initiatives. The present study investigates the correlation between the initial amount of funds raised on the first day of a fundraising campaign and the target amount of funds sought by the fundraiser, as well as the type of activities involved. The present study utilizes data derived from crowdfunding endeavors in Southeast Asian nations to scrutinize the funds amassed through donations by juxtaposing trends, cultures, and characteristics of fundraisers employing donation-based crowdfunding. The present investigation employs data collected between the period spanning from the beginning of September 2021 to the end of September 2021 in the economies of Southeast Asia, including Singapore, Indonesia, Malaysia, Thailand, and the Philippines. The present investigation utilizes the partial least squares structural equation modeling (PLS-SEM) approach for the estimation of the variables. The findings of the hypothesis indicate that there exists a positive correlation between crowdfunding, environmental nonprofit organizations, organizational profitability, and CSR.
Current research on environmental instruments often isolates the two mainstream types, market-based and regulation-based, overlooking their real-world interactions. In response, the intensity gap variable (EII_GAP) is constructed to link various instruments into a united system. Thus, based on the spatial econometrics of the spatial panel Durbin model (SPDM), the collective effects between market- and regulation-based environmental instruments on environmental quality are explored. Moreover, the political strategies for maximizing environmental benefits are discussed. Results show that the interaction pattern between market- and regulation-based environmental instruments on environmental quality is characterized by competition rather than cooperation. A unit widening in the intensity gap leads to 17 to 18% and 12 to 18% units of environmental quality improvement in local and adjacent areas, respectively. Furthermore, the "dominate-follow" approach as the most effective mode for maximizing environmental effects is proposed. This study recommends employing one type of instrument as the dominant while the other as the auxiliary. In provinces where one kind of environmental instrument takes domination, the environmental quality could be increased by around 8 to 113% after taking another contrary instrument as the auxiliary.
Adsorption is one of the most efficient methods for remediating industrial recalcitrant wastewater due to its simple design and low investment cost. However, the conventional adsorbents used in adsorption have several limitations, including high cost, low removal rates, secondary waste generation, and low regeneration ability. Hence, the focus of the research has shifted to developing alternative low-cost green adsorbents from renewable resources such as biomass. In this regard, the recent progress in the modification of biomass-derived adsorbents, which are rich in cellulosic content, through a variety of techniques, including chemical, physical, and thermal processes, has been critically reviewed in this paper. In addition, the practical applications of raw and modified biomass-based adsorbents for the treatment of industrial wastewater are discussed extensively. In a nutshell, the adsorption mechanism, particularly for real wastewater, and the effects of various modifications on biomass-based adsorbents have yet to be thoroughly studied, despite the extensive research efforts devoted to their innovation. Therefore, this review provides insight into future research needed in wastewater treatment utilizing biomass-based adsorbents, as well as the possibility of commercializing biomass-based adsorbents into viable products.
Although increased attempts to preserve biodiversity ecosystems have been widely publicized, bibliometric research of biodiversity loss remains limited. Using VOSviewer, we hope to provide a bibliometric assessment of global research trends on biodiversity loss from 1990 to 2021. Document type, language, publication trend, countries, institutions, Author Keywords, and Keywords Plus were all examined. This study recorded a total of 6599 publications from the Web of Science Core Collection database. According to the findings, biodiversity loss research is expected to rise dramatically in the near future. However, the role of social sciences and economics in biodiversity loss studies has received little attention. The USA made the most significant contribution in this field. Biological Conservation was the most productive journal, and Proceedings of the National Academy of Sciences of the United States of America was the most influential journal in biodiversity loss literature. Eisenhauer, N was the most prolific author, and Collen, B was the most referenced. Biodiversity, biodiversity loss mechanisms, biodiversity loss drivers, conservation, and climate change have been the topic of previous research. Possible future research hotspots may include species diversity and many elements of biodiversity. Lastly, the outcomes of this study suggest that existing socio-economic concerns can be integrated into decision-making processes to improve biodiversity conservation.
South and Southeast Asia is by far the most populous region in Asia, with the greatest number of threatened species. Changes in habitat are a major contributor to biodiversity loss and are more common as a result of land-use changes. As a result, the goal of this study is to use negative binomial regression models to investigate habitat change as one of the important drivers of biodiversity loss in South and Southeast Asian countries from 2013 to 2018. According to the negative binomial estimates, the findings for the habitat change measures are quantitatively similar for the impacts of agricultural land and arable land on biodiversity threats. Agricultural and arable land both have a positive impact on biodiversity loss. We found that, contrary to our expectations, the forest area appears to have an unexpected direct influence on the number of threatened species. A higher number of threatened species is associated with rising per capita income, human population and a low level of corruption control. Finally, the empirical findings are consistent across taxonomic groups, habitat change measures and Poisson-based specifications. Some policy implications that could mitigate biodiversity loss include educating and promoting good governance among the population and increase the conservation effort to sustain green area and national forest parks in each country.
Submarine landslides, also known as submarine mass failures (SMFs), are major natural marine disasters that could critically damage coastal facilities such as nuclear power plants and oil and gas platforms. It is therefore essential to investigate submarine landslides for potential tsunami hazard assessment. Three-dimensional seismic data from offshore Brunei have revealed a giant seabed mass deposited by a previous SMF. The submarine mass extends over 120 km from the continental slope of the Baram Canyon at 200 m water depth to the deep basin floor of the Northwest Borneo Trough. A suite of in-house two-dimensional depth-averaged tsunami simulation model TUNA (Tsunami-tracking Utilities and Application) is developed to assess the vulnerability of coastal communities in Sabah and Sarawak subject to potential SMF tsunami. The submarine slide is modeled as a rigid body moving along a planar slope with the center of mass motion parallel to the planar slope and subject to external forces due to added mass, gravity, and dissipation. The nonlinear shallow water equations are utilized to simulate tsunami propagation from deepwater up to the shallow offshore areas. A wetting-drying algorithm is used when a tsunami wave reaches the shoreline to compute run up of tsunami along the shoreline. Run-up wave height and inundation maps are provided for seven densely populated locations in Sabah and Sarawak to highlight potential risks at each location, subject to two scenarios of slide slopes: 2° and 4°. The first wave may arrive at Kudat as early as 0.4 h after the SMF, giving local communities little time to evacuate. Over a small area, maximum inundated depths reaching 20.3 m at Kudat, 26.1 m at Kota Kinabalu, and 15.5 m at Miri are projected, while the maximum inundation distance of 4.86 km is expected at Miri due to its low-lying coast. In view of the vulnerability of some locations to the SMF tsunami, it is important to develop and implement community resilience program to reduce the potential damage that could be inflicted by SMF tsunamis.
This study aimed to determine bioavailable heavy metal concentrations (As, Cd, Co, Cu, Cr, Ni, Pb, Zn) and their potential sources in classroom dust collected from children's hand palms in Rawang (Malaysia). This study also aimed to determine the association between bioavailable heavy metal concentration in classroom dust and children's respiratory symptoms. Health risk assessment (HRA) was applied to evaluate health risks (non-carcinogenic and carcinogenic) due to heavy metals in classroom dust. The mean of bioavailable heavy metal concentrations in classroom dust found on children's hand palms was shown in the following order: Zn (1.25E + 01 μg/g) > Cu (9.59E-01 μg/g) > Ni (5.34E-01 μg/g) > Cr (4.72E-02 μg/g) > Co (2.34E-02 μg/g) > As (1.77E-02 μg/g) > Cd (9.60E-03 μg/g) > Pb (5.00E-03 μg/g). Hierarchical cluster analysis has clustered 17 sampling locations into three clusters, whereby cluster 1 (S3, S4, S6, S15) located in residential areas and near to roads exposed to vehicle emissions, cluster 2 (S10, S12, S9, S7) located near Rawang town and cluster 3 (S13, S16, S1, S2, S8, S14, S11, S17, S5) located near industrial, residential and plantation areas. Emissions from vehicles, plantations and industrial activities were found as the main sources of heavy metals in classroom dust in Rawang. There is no association found between bioavailable heavy metal concentrations and respiratory symptoms, except for Cu (OR = 0.03). Health risks (non-carcinogenic and carcinogenic risks) indicated that there are no potential non-carcinogenic and carcinogenic risks of heavy metals in classroom dust toward children health.