Risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 over 2017-2022 in Sanandaj, an area of Iran.

The risk of cardiovascular and respiratory diseases attributed to satellite-based PM2.5 has been less investigated. In this study, the attributable risk was estimated in an area of Iran. The predicted air PM2.5 using satellite data and a two-stage regression model was used as the predictor of the diseases. The dose-response linkage between the bias-corrected predictor employing a strong statistical approach and the outcomes was evaluated using the distributed lag nonlinear model. We considered two distinct scenarios of PM2.5 for the risk estimation. Alongside the risk, the attributable risk and number were estimated for different levels of PM2.5 by age and gender categories. The cumulative influence of PM2.5 particles on respiratory illnesses was statistically significant at 13-16 µg/m3 relative to the reference value (median), mostly apparent in the middle delays. The cumulative relative risk of 90th and 95th percentiles were 2.03 (CI 95%: 1.28, 3.19) and 2.25 (CI 95%: 1.28, 3.96), respectively. Nearly 600 cases of the diseases were attributable to the non-optimum values of the pollutant during 2017-2022, of which more than 400 cases were attributed to high values range. The predictor's influence on cardiovascular illnesses was along with uncertainty, indicating that additional research into their relationship is needed. The bias-corrected PM2.5 played an essential role in the prediction of respiratory illnesses, and it may likely be employed as a trigger for a preventative strategy.

Seasonal variation of Covid-19 incidence and role of land surface and air temperatures: a case study in the west of Iran.

.In this study, we assessed the impact of satellite-based Land Surface Temperature (LST) and Air Temperature (AT) on covid-19. First, we spatio-temporally kriged the LST and applied bias correction. The epidemic shape, timing, and size were compared after and before adjusting for the predictors. Given the non-linear behavior of a pandemic, a semi-parametric regression model was used. In addition, the interaction effect between the predictors and season was assessed. Before adjusting for the predictors, the peak happened at the end of hot season. After adjusting, it was attenuated and slightly moved forward. Moreover, the Attributable Fraction (AF) and Peak to Trough Relative (PTR) were % 23 (95% CI; 15, 32) and 1.62 (95%CI; 1.34, 1.97), respectively. We found that temperature might have changed the seasonal variation of covid-19. However, given the large uncertainty after adjusting for the variables, it was hard to provide conclusive evidence in the region we studied.

Estimation of PM2.5 using high-resolution satellite data and its mortality risk in an area of Iran.

Satellite-based exposure of fine particulate matters has been seldom used as a predictor of mortality. PM2.5 was predicted using Aerosol Optical Depths (AOD) through a two-stage regression model. The predicted PM2.5 was corrected for the bias using two approaches. We estimated the impact by two different scenarios of PM2.5 in the model. We statistically found different distributions of the predicted PM2.5 over the region. Compared to the reference value (5 µg/m3), 90th and 95th percentiles had significant adverse effect on total mortality (RR 90th percentile:1.45; CI 95%: 1.08-1.95 and RR 95th percentile:1.53; CI 95%: 1.11-2.1). Nearly 1050 deaths were attributed to any range of the air pollution (unhealthy range), of which more than half were attributed to high concentration range. Given the adverse effect of extreme values compared to the both scenarios, more efforts are suggested to define local-specific reference values and preventive strategies.

Summer heat waves and their mortality risk over a 14-year period in a western region of Iran.

Compared to previous decade, impact of heat waves (HWs) on mortality in recent years needs to be discussed in Iran. We investigated temporal change in added impact of summer HWs on mortality in eight cities of Iran. The pooled length of HWs was compared between 2015-2022 and 2008-2014 using random and fixed-effects of meta-analysis regression model. The temporal change in impact of HWs was evaluated through interaction effect between crossbasis function of HW and year in a two-stage time varying model. In order to pool the reduced coefficients of each period, multivariate meta-regression model, including city-specific temperature and temperature range as heterogenicity factors, was used. In addition to relative risk (RR), attributable fraction (AF) of HW in the two periods was also estimated in each city. In the last years, the frequency of all HWs was higher and the weak HWs were significantly longer. The only significant RR was related to the lowest and low severe HWs which was observed in the second period. In terms of AF, compared to the strong HWs, all weak HWs caused a considerable excess mortality in all cities and second period. The subgroup analysis revealed that the significant impact in the second period was mainly related to females and elderlies. The increased risk and AF due to more frequent and longer HWs (weak HWs) in the last years highlights the need for mitigation strategies in the region. Because of uncertainty in the results of severe HWs, further elaborately investigation of the HWs is need.

Role of air pollution on seasonal distribution of Covid-19: a case study in the west of Iran.

The seasonal distribution of SARS-CoV-2 might be affected by air pollution. To test the hypothesis, epidemic determinants, namely, shape, timing (Peak and Trough) and size (Peak to Trough Ratio and Excess Risk) of seasonal distribution of the outbreak were compared before and after adjusting for air pollutants in a distributed lag nonlinear model. We controlled for one-lagged outcome and meteorological parameters in the model. We also evaluated interaction effect between air pollutants and season using stratification method. The epidemic determinants were changed after adjusting for PM2.5 and O3 in the model, suggesting the existence of their association with the seasonal distribution of the outbreak. The Excess Risk of season (i.e., the proportion of confirmed Covid-19 cases that were attributed to season; AF) was increased as %4 (%95 CI - 29, 38) after adjusting for PM2.5. Adjusting for O3 in the model resulted in % 1 (%95 CI - 36, 34) decrease in the index. NO, NO2 and SO2 had no association with the seasonal distribution, though the interaction analysis revealed that association of NO2 and SO2 with Covid-19 confirmed cases were significantly higher in fall than winter and spring, respectively. Totally, PM2.5 has negatively associated with the seasonal distribution of the outbreak while O3 has positively associated in the region under study. Although some reasons such as wearing mask and oxidative effect might go before the negative and positive associations, but our results suggests that any association and causation between air pollution and Covid-19 should be carefully interpreted.

Immobilization of microorganisms in activated zeolite beads and alkaline pretreated straws for ammonium-nitrogen removal from urban river water.

The non-treated wastewater from residential areas contains high concentrations of ammonium-nitrogen (NH4+-N). When discharged into the drainage water system, it deteriorates the water quality in urban rivers. This study used two types of materials to form eco-bags, using activated zeolite bead (AZB) and alkaline pretreated straw (APS), in geotextile bags for easy recovery and reuse. The AZB and APS provided the breeding habitat for the microorganisms that promoted biofilm formation on their surface. The immobilization of engineered denitrification microorganisms facilitated the removal of NH4+-N from the urban river water. The NH4+-N removal in the AZB and APS bags were in the range of 64-73%, and 56-61%, respectively, while the chemical oxygen demand (COD) removal in the AZB and APS bags ranged from 33-36%, and 30-31%, respectively. In addition, as evident from DNA and microbial community analysis, the microorganisms demonstrated a greater proclivity to grow and proliferate on the surface of AZB and APS and improved the water quality of urban rivers.

Pectin/chitosan/tripolyphosphate encapsulation protects the rat lung from fibrosis and apoptosis induced by paraquat inhalation.

BACKGROUND: Paraquat poisoning leads to lung injury and pulmonary fibrosis. The effect of paraquat encapsulation by previously described Pectin/Chitosan/Tripolyphosphate nanoparticles on its pulmonary toxicity was investigated in present study in a rat model of poison inhalation. MATERIAL AND METHOD: The rats inhaled nebulized different formulation of paraquat (n = 5) for 30 min in various experimental groups. Lung injury and fibrosis scores, Lung tissue enzymatic activities, apoptosis markers were determined compared among groups. RESULTS: Encapsulation of paraquat significantly rescued both lung injury and fibrosis scores. Lung MDA level was reduced by encapsulation. Paraquat poisoning led to lung tissue apoptosis as was evidenced by higher Caspase-3 and Bax/Bcl2 expressions in rats subjected to paraquat inhalation instead of normal saline or free nanoparticles. Again, nanoencapsulation reduced these apoptosis markers significantly. Alpha-SMA expression was also reduced by encapsulation. Nanoparticles per se have no or little toxicity as was evidenced by inflammatory and apoptotic markers and histological scores. CONCLUSION: In a rat model of inhalation toxicity of paraquat, loading of this herbicide on PEC/CS/TPP nanoparticles reduced acute lung injury and fibrosis. The encapsulation also led to lower apoptosis, oxidative stress and alpha-SMA expression in the lung tissue.

Influence of iron mining activity on heavy metal contamination in the sediments of the Aqyazi River, Iran.

In order to investigate the degree of contamination of heavy metals (As, Cd, Cr, Cu, Fe, Pb, and Ni) in the Aqyazi River in Iran, sediment samples were collected from the river receiving wastewater from an iron-manufacturing plant. For this study, contamination indices, geoaccumulation index (Igeo), contamination factor (CF), and pollution load index (PLI), were used to assess contamination by the heavy metals. The results of the Igeo indicated that the sediments were moderately contaminated by Cu and strongly to extremely contaminated by Cd. Based on spatial distribution of concentrations and the Igeo, mining activity was the source of Cu and Cd in the Aqyazi River. Furthermore, the elevated Igeo of Cd at upmost northern station was not influenced by the mining activity, suggesting that there may be another upstream anthropogenic source of Cd. The CF values indicated the same trend as the Igeo. The PLI was calculated using all the metals analyzed in this study, and displayed that the sediments were not polluted. However, the PLI was re-calculated using only Cu and Cd and indicated that the sediments were polluted. Our results suggest further studies to trace another source of Cd upstream of the Aqyazi River and to investigate influence of the river waters on accumulation of heavy metals in soils and vegetables downstream.

Correction to: Influence of iron mining activity on heavy metal contamination in the sediments of the Aqyazi River, Iran.

The original version of this article unfortunately contained an error in the affiliation section and missing acknowledgment statement.

Application of cadmium-doped ZnO for the solar photocatalytic degradation of phenol.

In this study, photocatalysis of phenol was studied using Cd-ZnO nanorods, which were synthesized by a hydrothermal method. The Cd-ZnO photocatalyst was characterized using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy dispersive X-ray spectroscopy, and Fourier transform infrared (FT-IR) and UV-Vis spectroscopy. XRD patterns exhibit diffraction peaks indexed to the hexagonal wurtzite structures with the P63mc space group. SEM images showed that the average size of the Cd-ZnO nanorods was about 90 nm. Moreover, the nanorods were not agglomerated and were well-dispersed in the aqueous medium. FT-IR analysis confirmed that a surface modifier (n-butylamine) did not add any functional groups onto the Cd-ZnO nanorods. The dopant used in this study showed reduction of the bandgap energy between valence and conduction of the photocatalyst. In addition, effect of various operational parameters including type of photocatalyst, pH, initial concentration of phenol, amount of photocatalyst, and irradiation time on the photocatalytic degradation of phenol has been investigated. The highest phenol removal was achieved using 1% Cd-ZnO for 20 mg/l phenol at pH 7, 3 g/l photocatalyst, 120 min contact time, and 0.01 mole H2O2.

Isolation and identification of the native population bacteria for bioremediation of high levels of arsenic from water resources.

Health of millions of people is threatened by the risk of drinking arsenic-contaminated water worldwide. Arsenic naturally conflicts with the concept of life, but recent studies showed that some microorganisms use toxic minerals as the source of energy. Hence, the researchers should consider the development of cost-effective and highly productive procedures to remove arsenic. The current study was conducted on a native bacterial population of Seyed-Jalaleddin Spring Kurdistan, Iran. Accordingly, the arsenic amount in water samples was measured >500 µg/L by the two field and in vitro methods. Water samples were transferred to laboratory and cultured on chemically defined medium (CDM) with arsenic salts. A total of 14 native arsenic-resistant bacterial strains were isolated and after providing pure culture and performing biochemical tests, the isolates were identified using polymerase chain reaction (PCR) and 16s rRNA genomic sequencing. The potential of bacterial strains for the biotransformation of arsenic was assessed by the qualitative assessment of AgNO3 method and efficiency of arsenic speciation was determined for the first time by silver diethyldithiocarbamate (SDDC) method with an error of less than 5%. Among the isolated strains, only strain As-11 and strain As-12 showed arsenic transformation characteristics and were registered in NCBI database by the access numbers KY119262 and KY119261, respectively. Results of the current study indicated that strain As-11 had the potential of biotransformation of As(V) to As(III) and vice versa with the efficiency of 78% and 48%, respectively. On the other hand, strain As-12 had the potential for biotransformation of As(V) to As(III) and vice versa with the efficiency of 28% and 45%, respectively.

Isolation and identification of indigenous prokaryotic bacteria from arsenic-contaminated water resources and their impact on arsenic transformation.

Arsenic is a known human carcinogen. Arsenite [As(III), H3AsO3] and arsenate [As(V), H2AsO4- and HAsO42-] are the two predominant compounds of As found in surface water and groundwater. The aim of this study was to explore a bioremediation strategy for biotransformation of arsenite to arsenate by microorganisms. In this study, Babagorgor Spring, located west of Iran, was selected as the arsenic-contaminated source and its physicochemical characteristics and in situ microbiological composition were analyzed. Inductively Coupled Plasma-Atomic Emission Spectrometry (ICP-AES) analysis indicated that the arsenic level was 614µg/l. Fourteen arsenic tolerant indigenous bacteria were isolated from arsenic-contaminated water using chemically defined medium (CDM), supplemented with 260-3900mg/l arsenite and 1560-21800mg/l arsenate. Among the isolates, a strain As-11 exhibited high ability of arsenic transformation. Biochemical tests were used for bacterial identification and confirmation was conducted by 16S rRNA sequence analysis. Results confirmed that As-11 was related to the genus Pseudomonas. This bacterium showed maximum tolerable concentration to arsenite up to 3250mg/l and arsenate up to 20280mg/l. Under heterotrophic conditions, the bacterium exhibited 48% of As(III) and 78% of As(V) transformation from the medium amended with 130 and 312mg/l of sodium arsenite and sodium arsenate, respectively. Moreover, under chemolithotrophic conditions, bacterium was able to transform 41% of 130mg/l of As(III) from the medium amended with nitrate as the terminal electron acceptor. Pseudomonas strain As-11 was reported as an arsenic transformer, for the first time.

Density assessment and mapping of microorganisms around a biocomposting plant in Sanandaj, Iran.

Exposure to microorganisms can cause various diseases or exacerbate the excitatory responses, inflammation, dry cough and shortness of breath, reduced lung function, chronic obstructive pulmonary disease, and allergic response or allergic immune. The aim of the present study was to investigate the density of microorganisms around the air of processing facilities of a biocomposting plant. Each experiment was carried out according to ASTM E884-82 (2001) method. The samples were collected from inhaled air in four locations of the plant, which had a high traffic of workers and employees, including screen, conveyor belt, aerated compost pile, and static compost pile. The sampling was repeated five times for each location selected. The wind speed and its direction were measured using an anemometer. Temperature and humidity were also recorded at the time of sampling. The multistage impactor used for sampling was equipped with a solidified medium (agar) and a pump (with a flow rate of 28.3 l/m) for passing air through the media. It was found that the mean density of total bacteria was >1.7 × 103 cfu/m3 in the study area. Moreover, the mean densities of fungi, intestinal bacteria (Klebsiella), and Staphylococcus aureus were 5.9 × 103, 3.3 × 103, and 4.1 × 103 cfu/m3, respectively. In conclusion, according to the findings, the density of bacteria and fungi per cubic meter of air in the samples collected around the processing facilities of the biocomposting plant in Sanandaj City was higher than the microbial standard for inhaled air.

Copper Bioaccumulation and Depuration in Common Carp (Cyprinus carpio) Following Co-exposure to TiO2 and CuO Nanoparticles.

Metal oxide nanoparticles (NPs), such as TiO2 and CuO, are widely applied in an increasing number of products and applications, and therefore their release to the aquatic ecosystems is unavoidable. However, little is known about joint toxicity of different NPs on tissues of aquatic organisms, such as fish. This study was conducted to assess the uptake and depuration of Cu following exposure to CuO NPs in the presence of TiO2 NPs in the liver, intestine, muscle, and gill of common carp, Cyprinus carpio. Carps with a mean total length of 23 ± 1.5 cm and mean weight of 13 ± 1.3 g were divided into 6 groups of 15 each (1 control group) and exposed to TiO2 NPs, CuO NPs, and a mixture of TiO2 and CuO NPs for periods of 20 days for uptake and 10 days for depuration. The determination of total Cu concentration was carried out by an ICP-OES. The order of Cu uptake in different tissues of the carps was liver > gill > muscle > intestine in both levels of CuO NPs alone; results showed that the total Cu concentrations in the presence of TiO2 nanoparticles were increased and were in the sequence of liver > gill > intestine > muscle. In depuration period, Cu concentrations were decreased in all treatments in the sequence of gill > intestine > muscle > liver. Uptake of Cu in different tissues of common carp increased with increasing concentration and time and was tissues- and time-dependent. In conclusion, this study suggested that the uptake of Cu in the tissues of common carp increased in the joint presence of TiO2 NPs.

Subjective Mental Workload and Its Correlation With Musculoskeletal Disorders in Bank Staff.

OBJECTIVE: The purpose of this study was to evaluate the rate of subjective mental workload (SMWL) and its correlation with musculoskeletal disorders among bank staff members in Kurdistan Province located in western Iran. METHODS: This cross-sectional study was conducted among 200 bank staff members in Kurdistan Province, Iran. The mental workload was assessed using the National Aeronautics and Space Administration-Task Load Index (NASA-TLX) computerized version. NASA-TLX is a multidimensional rating procedure that derives an overall workload score based on a weighted average of ratings on 6 subscales. These subscales include Mental Demands, Physical Demands, Temporal Demands, Performance, Effort, Effectiveness, and Frustration. The musculoskeletal disorders (MSDs) were documented with the Nordic Musculoskeletal Questionnaire and generic body diagram. RESULTS: Of the staff members, 78.5% experienced pain at least once during the past year in 1 of their 9 musculoskeletal body regions. The highest frequencies of pain were in the neck and lower back. The NASA-TLX estimated the Effort and Performance scales with mean ± SD of 72.8 ± 25.2 and 36 ± 22.6, respectively, as the maximal and minimal scores among the 6 subscales of SMWL. The statistical analysis of the data revealed that there was a significant correlation between the overall mental workload score and also among the 6 subscales of SMWL separately with MSDs (P < .05). CONCLUSION: SMWL appears to be a risk factor in the incidence of MSDs, so that the odds of MSDs increased by 11% with each additional 1-point increase in SMWL score.

Histopathological effects following short-term coexposure of Cyprinus carpio to nanoparticles of TiO2 and CuO.

The aim of this research was to investigate the coexposure of nanoparticles of titanium dioxide (TiO2) and copper oxide (CuO) on the alterations of the gill, intestine, kidney, and liver tissues of carps (Cyprinus carpio). In this study, carps (length 23 ± 1.5 cm; weight 13 ± 1.3 g) were divided into six groups of 15 each and exposed to 2.5 and 5.0 mg L(-1) of CuO nanoparticles (NPs), 10.0 mg L(-1) of TiO2 NPs, and 2.5 and 5.0 mg L(-1) of CuO NPs + 10.0 mg L(-1) of TiO2 NP mixture. Fish were sampled for histopathological studies after hematoxylin-eosin staining. Results indicated that the more kinds of histopathology anomalies observed with CuO NP and TiO2 NP mixture were broadly of the same type as CuO NPs and TiO2 NPs alone, but the severity or incidence of injuries of gill, intestine, liver, and kidney of carps in the mixture of CuO NPs + TiO2 NPs was higher than that of each NP alone. Moreover, behavioral changes in carps exposed to CuO NP and TiO2 NP mixture such as hyperactivity, loss of balance, and convulsions were higher than those to CuO NPs and TiO2 NPs alone. In conclusion, the presence of TiO2 NPs enhanced the effects of NPs of copper oxide in terms of histopathological changes in carps.

Biodegradation of 2,4-dichlorophenoxyacetic acid by bacteria with highly antibiotic-resistant pattern isolated from wheat field soils in Kurdistan, Iran.

Recently, there has been increasing interest to clean up the soils contaminated with herbicide. Our aim was to determine the bioremediation of 2,4-dichlorophenoxyacetic acid (2,4-D) from wheat fields which have a long history of herbicide in Sanandaj. Based on our literature survey, this study is the first report to isolate and identify antimicrobial resistant bacteria from polluted wheat field soils in Sanandaj which has the capacity to degrade 2,4-D. From 150 2,4-D-exposed soil samples, five different bacteria were isolated and identified based on biochemical tests and 16S ribosomal RNA (rRNA). Pseudomonas has been the most frequently isolated genus. By sequencing the 16S rRNA gene of the isolated bacteria, the strains were detected and identified as a member of the genus Pseudomonas sp, Entrobacter sp, Bacillus sp, Seratia sp, and Staphylococcus sp. The sequence of Sanandaj 1 isolate displayed 87% similarity with the 16S rRNA gene of a Pseudomonas sp (HE995788). Similarly, all the isolates were compared to standard strains based on 16S rRNA. Small amounts of 2,4-D could be transmitted to a depth of 10-20 cm; however, in the depth of 20-40 cm, we could not detect the 2,4-D. The isolates were resistant to various antibiotics particularly, penicillin, ampicillin, and amoxicillin.

Comparison of QSAR models based on combinations of genetic algorithm, stepwise multiple linear regression, and artificial neural network methods to predict Kd of some derivatives of aromatic sulfonamides as carbonic anhydrase II inhibitors.

Four stepwise multiple linear regressions (SMLR) and a genetic algorithm (GA) based multiple linear regressions (MLR), together with artificial neural network (ANN) models, were applied for quantitative structure-activity relationship (QSAR) modeling of dissociation constants (Kd) of 62 arylsulfonamide (ArSA) derivatives as human carbonic anhydrase II (HCA II) inhibitors. The best subsets of molecular descriptors were selected by SMLR and GA-MLR methods. These selected variables were used to generate MLR and ANN models. The predictability power of models was examined by an external test set and cross validation. In addition, some tests were done to examine other aspects of the models. The results show that for certain purposes GA-MLR is better than SMLR and for others, ANN overcomes MLR models.

Machine learning-based modeling of malachite green adsorption on hydrochar derived from hydrothermal fulvification of wheat straw.

This study investigated the efficiency of hydrochar derived from hydrothermal fulvification of wheat straw in adsorbing malachite green (MG) dye. The characterizations of the hydrochar samples were determined using various analytical techniques like SEM, EDX, FTIR, X-ray spectroscopy, BET surface area analysis, ICP-OES for the determination of inorganic elements, elemental analysis through ultimate analysis, and HPLC for the content of sugars, organic acids, and aromatics. Adsorption experiments demonstrated that hydrochar exhibited superior removal efficiency compared to feedstock. The removal efficiency of 91 % was obtained when a hydrochar dosage of 2 g L-1 was used for 20 mg L-1 of dye concentration in a period of 90 min. The results showed that the study data followed the Freundlich isotherms as well as the pseudo-second order kinetic model. Moreover, the determined activation energy of 7.9 kJ mol-1 indicated that the MG adsorption was a physical and endothermic process that increased at elevated temperatures. The study also employed an artificial neural network (ANN), a machine learning approach that achieved remarkable R2 (0.98 and 0.99) for training and validation dataset, indicating high accuracy in simulating MG adsorption by hydrochar. The model's sensitivity analysis demonstrated that the adsorbent dosage exerted the most substantial influence on the adsorption process, with MG concentration, pH, and time following in decreasing order of impact.

Photocatalytic removal of 2,4-Dichlorophenoxyacetic acid from aqueous solution using tungsten oxide doped zinc oxide nanoparticles immobilised on glass beads.

Groundwater is the only source of high quality water for human consumption in most parts of the world; however, it can be easily contaminated by domestic, industrial, and agricultural wastes such as fertilisers and pesticides. The main objective of the present research was to study the photocatalytic removal of 2,4-Dichlorophenoxyacetic acid pesticide (2,4-D) from aqueous media. This was a laboratory scale study in which the zinc oxide nanoparticles were doped with 0.5, 1, and 2 molar percent of tungsten oxide. The nanoparticles synthesised were characterised using powder XRD, SEM, FTIR, and UV-Vis Spectroscopy analyses. During the photodegradation of 2,4-D, the operational parameters studied were pH, nanoparticles dosage, initial pesticide concentration, light intensity, contact time, and the mineralisation trend of organic matter. It was found that the doped nanoparticles had a smaller band gap energy, which confirms the effect of doping. The percentage of the dopant can affect the pesticide removal efficiency. The optimal pH value obtained was 7. In addition, the process efficiency, increased from 27% to 78% with increasing UV light intensity from 172 to 505 W/m2 respectively. Moreover, it was found that, with increasing light intensity, contact time and nanoparticle concentration all caused the pesticide removal efficiency to be increased too. In addition, the increase of the pesticide concentration would cause a reduction in the process removal efficiency. This study indicated that the photocatalytic process using tungsten doped zinc oxide nanoparticles can remove the 2,4-D pesticide by around 80% from the aquatic environment.