Concentrations of 11 trace elements (V, Cr, Co, Ni, Cu, Zn, As, Ag, Cd, Pb, and U) were determined in the intertidal surface sediments of Peninsular Malaysia. The average trace element concentrations are ranked as follows: Zn>V>As>Cr>Pb>Cu>Ni>Co>U>g>Cd. Interim Sediment Quality Guidelines (ISQGs) employed in present study are the Australia and New Zealand joint guideline (ANZECC/ARMCANZ), and the Hong Kong authorities. From the pooled data, none of these trace elements have the average concentration above the ISQG-high values. However, As and Ag average concentrations were over the ISQG-low values. Some elements were found to have the average concentration above the ISQG-high and/or ISQG-low in certain locations, including Kampung Pasir Putih (JPP), Lumut Port (ALP), Kuala Perai (PKP), Port Dickson (NPD), and others. The lowest and highest concentrations in a specific sampling location and maritime area varied among the elements, variations that were greatly affected by natural and anthropogenic activities in a given area. For each trace element, there were various levels of concentration among the sampling locations and maritime areas. These patterns indicated pollutant sources of an element for each area perhaps derived from nearby areas and did not widely distributed to other locations. It is necessary for Malaysia to develop an ISQG for effective quick screening and evaluation of the coastal environment of Peninsular Malaysia.
In the last few decades, environmental contaminants (ECs) have been introduced into the environment at an alarming rate. There is a risk to human health and aquatic ecosystems from trace levels of emerging contaminants, including hospital wastewater (HPWW), cosmetics, personal care products, endocrine system disruptors, and their transformation products. Despite the fact that these pollutants have been introduced or detected relatively recently, information about their characteristics, actions, and impacts is limited, as are the technologies to eliminate them efficiently. A wastewater recycling system is capable of providing irrigation water for crops and municipal sewage treatment, so removing ECs before wastewater reuse is essential. Water treatment processes containing advanced ions of biotic origin and ECs of biotic origin are highly recommended for contaminants. This study introduces the fundamentals of the treatment of tertiary wastewater, including membranes, filtration, UV (ultraviolet) irradiation, ozonation, chlorination, advanced oxidation processes, activated carbon (AC), and algae. Next, a detailed description of recent developments and innovations in each component of the emerging contaminant removal process is provided.
Most of the developing countries have solid waste management problems. Solid waste strategic planning requires accurate prediction of the quality and quantity of the generated waste. In developing countries, such as Malaysia, the solid waste generation rate is increasing rapidly, due to population growth and new consumption trends that characterize society. This paper proposes an artificial neural network (ANN) approach using feedforward nonlinear autoregressive network with exogenous inputs (NARX) to predict annual solid waste generation in relation to demographic and economic variables like population number, gross domestic product, electricity demand per capita and employment and unemployment numbers. In addition, variable selection procedures are also developed to select a significant explanatory variable. The model evaluation was performed using coefficient of determination (R(2)) and mean square error (MSE). The optimum model that produced the lowest testing MSE (2.46) and the highest R(2) (0.97) had three inputs (gross domestic product, population and employment), eight neurons and one lag in the hidden layer, and used Fletcher-Powell's conjugate gradient as the training algorithm.
Vibrio species isolated from four different sampling stations in the west coast of Peninsular Malaysia were screened for their antimicrobial resistance and plasmid profiles. A total of 138 isolates belonging to 15 different species were identified. Vibrio campbellii, V. parahaemolyticus, V. harveyi, and V. tubiashii were found to predominance species at all stations. High incidence of erythromycin, ampicillin, and mecillinam resistance was observed among the Vibrio isolates. In contrast, resistance against aztreonam, cefepime, streptomycin, sulfamethoxazole, and sulfonamides was low. All the Vibrio isolates in this study were found to be susceptible to imipenem, norfloxacin, ofloxacin, chloramphenicol, trimethoprim/sulfamethoxazole, and oxytetracycline. Ninety-five percent of the Vibrio isolates were resistant to one or more different classes of antibiotic, and 20 different resistance antibiograms were identified. Thirty-two distinct plasmid profiles with molecular weight ranging from 2.2 to 24.8 kb were detected among the resistance isolates. This study showed that multidrug-resistant Vibrio spp. were common in the aquatic environments of west coast of Peninsular Malaysia.
This study presents the use of a wavelet-based time series model to forecast the daily average particulate matter with an aerodynamic diameter of less than 10 μm (PM10) in Peninsular Malaysia. The highlight of this study is the use of a discrete wavelet transform (DWT) in order to improve the forecast accuracy. The DWT was applied to convert the highly variable PM10 series into more stable approximations and details sub-series, and the ARIMA-GARCH time series models were developed for each sub-series. Two different forecast periods, one was during normal days, while the other was during haze episodes, were designed to justify the usefulness of DWT. The models' performance was evaluated by four indices, namely root mean square error, mean absolute percentage error, probability of detection and false alarm rate. The results showed that the model incorporated with DWT yielded more accurate forecasts than the conventional method without DWT for both the forecast periods, and the improvement was more prominent for the period during the haze episodes.
It has been widely reported that allozyme frequency variation is a potential indicator of heavy metal-induced impacts in aquatic populations. In the present study, wild populations of horseshoe crab (Carcinoscorpius rotundicauda) were collected from contaminated and uncontaminated sites of Peninsular Malaysia. By adopting horizontal starch gel electrophoresis, seven enzyme systems were used to study allozyme polymorphisms. Nine polymorphic loci were observed in C. rotundicauda. The relationships of allozyme variations with the concentrations of Cd, Cu, Ni, and Zn in sediments and in muscle tissues of horseshoe crabs were determined. Based on genetic distance, the lower mean value of Nei's D (0.017) indicated that both of the contaminated populations of Kg. Pasir Puteh and Kuala Juru were very closely related when compared to the relatively uncontaminated Pantai Lido population. Higher heterozygosities were shown by the contaminated populations when compared to the uncontaminated population. Different allelic frequencies could be observed for the aldolase (ALD; E.C. 18.104.22.168) locus between the contaminated and uncontaminated populations of C. rotundicauda. The dendrogram of genetic relationships of the three populations of C. rotundicauda showed the same clustering pattern as the dendrograms are based on heavy metals in the sediments and in the horseshoe crabs' abdominal muscles. From the F statistics, the present study showed that the three populations of horseshoe crabs were considered to have undergone moderate genetic differentiation with a mean F (ST) value of 0.092 .The current results suggest that allozyme polymorphism in horseshoe crabs is a potential biomonitoring tool for metal contamination, although further validation is required.
Surface sediments were collected from the north western aquatic area (13 intertidal sites and 5 river drainages) of Peninsular Malaysia, which were suspected to have received different anthropogenic sources. These sites included town areas, ports, fishing village, industrial areas, highway sides, jetties and some relatively unpolluted sites. The present study revealed that 4.79-32.91 μg/g dry weight for Cu, 15.85-61.56 μg/g dry weight for Pb, and 33.6-317.4 μg/g dry weight for Zn based on 13 intertidal surface sediments while those based on 5 river drainage surface sediments were 10.24-119.6 μg/g dry weight for Cu, 26.7-125.7 μg/g dry weight for Pb and 88.7-484.1 μg/g dry weight for Zn. In general, the metal levels in the drainage sediments are higher than in the intertidal sediments, suggesting dilution factor in the intertidal sediment and direct effluent from point sources in the drainage sediment. In particular, the total concentrations of Cu, Pb, and Zn for the sampling site at Kuala Kurau Town exceeded the Effect Range Median values for Cu, Pb, and Zn for assessments of sediment quality values for freshwater sediment as proposed by MacDonald et al. (Arch Environ Contam Toxicol 39:20-31, 2000), thus adverse biological effects would be observed above this level. Assessment using enrichment factor (using Fe as a normalizer) and geoaccumulation index showed that the three metals at Kuala Kurau Town and Juru Industry drainage were evidenced as having more enrichment and mostly due to non-natural sources. However, caution should be exercised that the interpretation can only become valid when the ratios, indices, and sediment quality values are combined. This is due to the fact that not all the established indices are applicable and, to a certain extent, some of them should be further revised and improved to suit a different metal for Malaysian sediment. Undoubtedly, sites near drainages at Kuala Kurau Town and Juru River Basin need greater attention to mitigate the heavy metal pollution in the future.
Multivariate analysis including correlation, multiple stepwise linear regression, and cluster analyses were applied to investigate the heavy metal concentrations (Cd, Cu, Fe, Ni, Pb, and Zn) in the different parts of bivalves and gastropods. It was also aimed to distinguish statistically the differences between the marine bivalves and the gastropods with regards to the accumulation of heavy metals in the different tissues. The different parts of four species of bivalves and four species of gastropods were obtained and analyzed for heavy metals. The multivariate analyses were then applied on the data. From the multivariate analyses conducted, there were correlations found between the soft tissues of bivalves and gastropods, but none was found between the shells and the soft tissues of most of the molluscs (except for Cerithidea obtusa and Puglina cochlidium). The significant correlations (P < 0.05) found between the soft tissues were further complemented by the multiple stepwise linear regressions where heavy metals in the total soft tissues were influenced by the accumulation in the different types of soft tissues. The present study found that the distributions of heavy metals in the different parts of molluscs were related to their feeding habits and living habitats. The statistical approaches proposed in this study are recommended for use in biomonitoring studies, since multivariate analyses can reduce the cost and time involved in identifying an effective tissue to monitor the heavy metal(s) bioavailability and contamination in tropical coastal waters.
Fish tilapia Oreochromis mossambicus were collected from a contaminated Seri Serdang (SS) pond potentially receiving domestic effluents and an uncontaminated pond from Universiti Putra Malaysia (UPM). The fish were dissected into four parts namely gills, muscles, intestines, and liver. All the fish parts were pooled and analyzed for the concentrations of Cd, Cu, Fe, Ni, Pb, and Zn. Generally, the concentrations of all metals were low in the edible muscle in comparison to the other parts of the fish. It was found that the levels of all the heavy metals in the different parts of fish collected from the SS were significantly (P<0.05) higher than those from UPM, indicating greater metal bioavailabilities in the SS pond. The sediment data also showed a similar pattern with significantly (P<0.05) higher metal concentrations in SS than in UPM, indicating higher metal contamination in SS. Potential health risk assessments based on provisional tolerable weekly intake (PTWI) and the amount of fish required to reach the PTWI values, estimated daily intake (EDI), and target hazard quotient (THQ) indicated that health risks associated with heavy metal exposure via consumption of the fish's muscles were insignificant to human. Therefore, the consumption of the edible muscles of tilapia from both ponds should pose no toxicological risk of heavy metals since their levels are also below the recommended safety guidelines. While it is advisable to discard the livers, gills, and intestines of the two tilapia fish populations before consumption, there were no potential human health risks of heavy metals to the consumers on the fish muscle part.
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.
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.
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.
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.
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.
Oil palm agriculture has caused extensive land cover and land use changes that have adversely affected tropical landscapes and ecosystems. However, monitoring and assessment of oil palm plantation areas to support sustainable management is costly and labour-intensive. This study used an unmanned aerial vehicles (UAV) to map smallholder farms and applied multi-criteria analysis to data generated from orthomosaics, to provide a set of sustainability indicators for the farms. Images were acquired from a UAV, with structure from motion (SfM) photogrammetry then used to produce orthomosaics and digital elevation models of the farm areas. Some of the inherent problems using high spatial resolution imagery for land cover classification were overcome by using texture analysis and geographic object-based image analysis (OBIA). Six spatially explicit environmental metrics were developed using multi-criteria analysis and used to generate sustainability indicator layers from the UAV data. The SfM and OBIA approach provided an accurate, high-resolution (~5 cm) image-based reconstruction of smallholder farm landscapes, with an overall classification accuracy of 89%. The multi-criteria analysis highlighted areas with lower sustainability values, which should be considered targets for adoption of sustainable management practices. The results of this work suggest that UAVs are a cost-effective tool for sustainability assessments of oil palm plantations, but there remains the need to plan surveys and image processing workflows carefully. Future work can build on our proposed approach, including the use of additional and/or alternative indicators developed through consultation with the oil palm industry stakeholders, to support certification schemes such as the Roundtable on Sustainable Palm Oil (RSPO).
The marine aquaculture industry has caused a suite of adverse environmental consequences, including offshore eutrophication. However, little is known about the extent to which aquaculture effluents affect nearby wetland ecosystems. We carried out a field experiment in a mangrove stand located between two effluent-receiving creeks to estimate the extent to which marine aquaculture affects the soil nutrient distribution and plant nutrient status of adjacent mangroves. Carbon (C), nitrogen (N), and phosphorus (P) contents and C isotopic signatures were determined seasonally in creeks, pore water, surface soils, and in the leaves of the dominant mangrove species Kandelia obovata. The creeks exhibited nutrient enrichment (2.44 mg N L-1 and 0.09 mg P L-1 on average). The soils had N (from 1.40 to 2.70 g kg-1) and P (from 0.58 to 2.76 g kg-1) much greater than those of pristine mangrove forests. Combined analyses of the N:P ratio, nutrient resorption efficiency, and proficiency indicated that soil P met plant demands, but plants in most plots showed N limitation, suggesting that soil nutrient accumulation did not fundamentally impact the plant nutrient status. Collectively, this case study shows that marine aquaculture farms can affect adjacent mangrove stands even though their effluents are not directly discharged into the mangrove stands, but mangrove forests may have substantial buffering capabilities for long-term nutrient loading.
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.
In order to evaluate the water quality of one of the most polluted urban river in Malaysia, the Penchala River, performance of eight biotic indices, Biomonitoring Working Party (BMWP), BMWPThai, BMWPViet, Average Score Per Taxon (ASPT), ASPTThai, BMWPViet, Family Biotic Index (FBI), and Singapore Biotic Index (SingScore), was compared. The water quality categorization based on these biotic indices was then compared with the categorization of Malaysian Water Quality Index (WQI) derived from measurements of six water physicochemical parameters (pH, BOD, COD, NH3-N, DO, and TSS). The river was divided into four sections: upstream section (recreational area), middle stream 1 (residential area), middle stream 2 (commercial area), and downstream. Abundance and diversity of the macroinvertebrates were the highest in the upstream section (407 individual and H' = 1.56, respectively), followed by the middle stream 1 (356 individual and H' = 0.82). The least abundance was recorded in the downstream section (214 individual). Among all biotic indices, BMWP was the most reliable in evaluating the water quality of this urban river as their classifications were comparable to the WQI. BMWPs in this study have strong relationships with dissolved oxygen (DO) content. Our results demonstrated that the biotic indices were more sensitive towards organic pollution than the WQI. BMWP indices especially BMWPViet were the most reliable and could be adopted along with the WQI for assessment of water quality in urban rivers.
The upper catchment region of the Baram River in Sarawak (Malaysian Borneo) is undergoing severe land degradation due to soil erosion. Heavy rainfall with high erosive power has led to a number of soil erosion hotspots. The goal of the present study is to generate an understanding about the spatial characteristics of seasonal and annual rainfall erosivity (R), which not only control sediment delivery from the region but also determine the quantity of material potentially eroded. Mean annual rainfall and rainfall erosivity range from 2170 to 5167 mm and 1632 to 5319 MJ mm ha-1 h-1 year-1, respectively. Seasonal rainfall and rainfall erosivity range from 848 to 1872 mm and 558 to 1883 MJ mm ha-1 h-1 year-1 for the southwest (SW) monsoon, 902 to 2200 mm and 664 to 2793 MJ mm ha-1h-1year-1 for the northeast (NE) monsoon and 400 to 933 mm and 331 to 1075 MJ mm ha-1 h-1 year-1 during the inter-monsoon (IM) period. Linear regression, Spearman's Rho and Mann Kendall tests were applied. Considering the regional mean rainfall erosivity in the study area, all the methods show an overall non-significant decreasing trend (- 9.34, - 0.25 and - 0.30 MJ mm ha-1 h-1 year-1, respectively for linear regression, Spearman's Rho and Mann Kendall tests). However, during SW monsoon and IM periods, rainfall erosivity showed a non-significant decreasing trend (- 25.45, - 0.52, - 0.40, and - 8.86, - 1.07, - 0.77 MJ mm ha-1 h-1 year-1, respectively) whereas in NE, monsoon season erosivity showed a non-significant increasing trend (14.90, 1.59 and 1.60 MJ mm ha-1 h-1 year-1, respectively). The mean erosivity density ranges from 0.77 to 1.38 MJ ha-1 h-1 year-1 and shows decreasing trend. Spatial distribution pattern of erosivity density indicates significantly higher occurrence of erosive rainfall in the lower elevation portion of the study area. The spatial pattern of mean rainfall erosivity trends (linear, Spearman's Rho and Mann Kendall) suggests that the study area can be divided into two zones with increasing rainfall erosivity trends in the northern zone and decreasing trends in the southern zone. These results can be used to plan conservation measures to reduce sediment delivery from localized soil erosion hotspots.
The ambient air of hospitals contains a wide range of biological and chemical pollutants. Exposure to these indoor pollutants can be hazardous to the health of hospital staff. This study aims to evaluate the factors affecting indoor air quality and their effect on the respiratory health of staff members in a busy Iranian hospital. We surveyed 226 hospital staff as a case group and 222 office staff as a control group. All the subjects were asked to fill in a standard respiratory questionnaire. Pulmonary function parameters were simultaneously measured via a spirometry test. Environmental measurements of bio-aerosols, particulate matter, and volatile organic compounds in the hospital and offices were conducted. T-tests, chi-square tests, and multivariable logistic regressions were used to analyze the data. The concentration of selected air pollutants measured in the hospital wards was more than those in the administrative wards. Parameters of pulmonary functions were not statistically significant (p > 0.05) between the two groups. However, respiratory symptoms such as coughs, phlegm, phlegmatic coughs, and wheezing were more prevalent among the hospital staff. Laboratory staff members were more at risk of respiratory symptoms compared to other occupational groups in the hospital. The prevalence of sputum among nurses was significant, and the odds ratio for the presence of phlegm among nurses was 4.61 times greater than office staff (p = 0.002). The accumulation of indoor pollutants in the hospital environment revealed the failure of hospital ventilation systems. Hence, the design and implementation of an improved ventilation system in the studied hospital is recommended.