Pollution Parameters Research Articles

Changes in antibiotic resistance patterns of Gram-negative bacilli across three different wastewater treatment plants in northwest Algeria; first comparative study.

Changes in antibiotic resistance of bacteria in three different wastewater treatment plants (WTP) were investigated to determine their role they play in the dissemination of multidrug resistant bacteria (MDR). Water samples were collected from downstream of three (WTP) from where pollution parameters were detected and high-performance Liquid chromatography (HPLC) coupled with an ultraviolet (UV) detector was used to measure antibiotic residues. In addition, 88 bacilli Gram-negative (BGN) were collected; activated sludge, aerated lagoons and natural lagoons, and tested for resistance against 18 βeta-lactam antibiotics. We evaluated the production of Extended-spectrum ß-lactamases (ESBLs) in isolates that were further confirmed by applying the automated VITEK-2 system. Among the 88 isolates, Klebsiella pneumoniae (60%),Escherichia coli (23,33%) and Serratia marcescens(32,14%) were the most isolated species. The abundance of the CTX ESBL-producers strains was significantly higher in natural lagoons that may have a greater influence on the occurrence and prevalence of MDR, with the MAR index being the lowest for Serratia marcescens (0,27) and the highest for Citrobacter koseri (0,88), serving as a hotspot environment for the evolution of Antibiotic resistance. Controlling or avoiding this kind of treatment can reduce the chance of MDRs entrance to the environment.

Potential Low-cost Treatment of Tannery Effluents from Industry by Adsorption on Activated Charcoal Derived from Olive Pomace

Tannery wastewater contains a significant amount of chemical compounds, including toxic substances. Due to the toxicity and negative environmental effects of these tannery effluents, mandatory treatment is necessary. The main objective of this study was to treat effluent from an artisanal tannery in the city of Fez (Morocco) using the adsorption process with activated charcoal derived from olive pomace. The physicochemical characterization of tanning water included several parameters, such as chemical oxygen demand (COD), total Kjeldahl nitrogen (TKN), suspended solids (SS), sulfate ions (SO42-), nitrate, and chromium Cr(VI). The analyses show that the adsorption process reduced nitrate by 57.54%, sulfate by 94.08%, TKN by 74.84%, COD by 68.18%, Cr by 91.27%, and Cr (VI) by 89.78%. The activated charcoal was characterized before and after tannery effluent treatment using various techniques, including FT-IR, SEM, and EDX. From the above, it can be inferred that using activated carbon made from olive pomace has the potential to reduce tannery effluent pollution parameters. This innovative approach demonstrates that competitive results can be achieved without sacrificing economic viability, thereby promoting sustainable practices in the treatment of industrial liquid waste and wastewater treatment plants.

Lie symmetry analysis on heat and mass transport aspects of rate type fluid flow with waste discharge concentration: Keller Box approach

The present analysis examines the effect of thermal radiation on the stream of Maxwell liquid past a stretchy surface in the presence of a zero-mass flux condition. The effect of waste discharge concentration, thermophoresis-Brownian motion, and porous media on the fluid motion is also considered. Investigation of the liquid flow with waste discharge concentration in stretched sheets may lead to the growth of effective methods for disposing of waste and preventing pollution. Due to their extensive applicability, the mechanisms of flow and heat transmission in the context of thermal radiation play a crucial role in research and industry. This phenomenon often occurs in spacecraft, nuclear reactor cooling, power production, aerospace technologies, combustion applications, gas turbines, hypersonic flights, and high-temperature processes. The modelled governing partial differential equations (PDEs) are converted into dimensionless ordinary differential equations (ODEs) utilizing appropriate similarity variables. Further, the Keller–Box approach is utilized to solve the resultant ODEs numerically. The influence of several parameters on the temperature, concentration and velocity profiles is shown via graphic representations. The intensification in Deborah’s number and porous parameter values declines the velocity profile. As the values of the radiation, thermophoresis, and Brownian motion parameters increase, the thermal profile increases. The concentration profile increases as the pollutant external source parameter value upsurges.

Evaluating the influence of sulphur dioxide and nitrogen dioxide in ambient air on biochemical factors contributing to plant tolerance.

Air pollutants, specifically, Sulphur Dioxide (SO2) and Nitrogen Dioxide (NO2) cause numerous biochemical changes in plants; by studying the biochemical parameters, plant resistance against these pollutants can be evaluated. The effect of air pollutants on biochemical parameters such as ascorbic acid (AA), total chlorophyll (TC), pH and the relative water content (RWC) of leaves has been investigated in the present study. The objective of the present study was to observe the relationship between biochemical parameters of plants and ambient air pollutants (SO2 and NO2). The morphological and biochemical changes of the sampled leaves of different plants were studied. Simultaneously, ambient air quality data was collected from the central pollution control board (CPCB) online portal. Among the sampled plants, Ficus religiosa and Morus alba plant species can be recommended as tolerant for the sampling sites: Jalandhar, Amritsar, and Ludhiana. Ficus benghalensis and Ficus religiosa can be recommended as tolerant for Chandigarh sector 25 and 53 respectively. As a result, it has been observed that SO2 has a negative effect on the relative water content and ascorbic acid content of most plant species, while NO2 can have ambiguous effect on plants (toxic and beneficial), and no strong association has been found with biochemical parameters. The current study provides pollutant-specific information and insights into the variations caused by air pollutants. The present study will contribute in suggesting the tolerant plant species that could provide new approaches for developing urban landscapes.

Enhanced biochemical treatment of pulping wastewater by Fenton process: Water quality optimization and detoxification efficiency

Pulping wastewater, characterized by enormous quantities, high pollution load and significant biotoxicity, making its effective treatment crucial for environmental protection. This study evaluated the performance of typical (anaerobic/aerobic) and advanced oxidation (Fenton) processes in pulping wastewater treatment, in relation to physicochemical parameters, acute toxicity and organic indexes. Raw wastewater contains numerous aromatic pollutants with benzene rings and unsaturated bonds, resulted in high pollution load and acute toxicity. Typical treatment process effectively reduced various pollution parameters, with toxicity was significantly decreased or completely removed (83.57–100.00 %). The residual toxicity unit (TU) of aerobic effluent for luminousbacteria and chlorella was only 2.76 and 1.23, respectively. Specifically, acute toxicity showed significantly positively correlated with total nitrogen (TN) and total organic carbon (TOC), positively associated with UV254, while negatively related to humification index (HIX). Although Fenton process optimized wastewater quality, it generated toxic intermediates that increased luminousbacteria toxicity to 5.12 TU, and Daphnia magna toxicity from 0.33 to 0.84 TU. Notably, newly organic pollutants such as Tricosane and Dibutyl phthalate may contributed to increased toxicity. In summary, these results concluded that advanced treatment processes may introduce unexpected toxicity and emphasized the importance of evaluate biotoxicity for environment protection.

Study of water pollution parameters in the dry and rainy seasons on the pollution index of the Mesuji River, Lampung, Indonesia

BackgroundThe Mesuji River is a vital natural water resource, yet increasing human activities have resulted in significant pollution through chemical, physical, and biological contamination. This study aimed to assess the water quality of the Mesuji River by analyzing key variables: TSS (Total Suspended Solids), pH, BOD (Biochemical Oxygen Demand), COD (Chemical Oxygen Demand), DO (Dissolved Oxygen), phosphate, fecal coliform, total coliform, and heavy metals MethodsBetween September 2022 and February 2023, water samples were collected from seven stations along the Mesuji River in Lampung Province, including Keagungan Dalam Village (MS-01), Sri Tanjung Village (MS-02), Badak Village (MS-03), Wiralaga II Village (MS-04), Gajah Mati Village (MS-05), Gebang River (MS-06), and the confluence of the Gebang and Mesuji Rivers (MS-07). Water quality variables were measured using standard methods and analyzed with descriptive statistics. ResultsMean values for TSS ranged from 3.71 ± 3.15 to 4.60 ± 2.51 mg/L, pH from 4.16 ± 0.20 to 4.22 ± 0.17, BOD from 2.30 ± 0.76 to 7.53 ± 1.96 mg/L, COD from 7.60 ± 4.30 to 34.19 ± 9.49 mg/L, and DO from 3.27 ± 0.42 to 3.33 ± 0.78 mg/L. Phosphate levels were between 0.25 ± 0.11 and 0.47 ± 0.45 mg/L, while fecal coliform and total coliform ranged from 12.91 ± 4.22 to 22.97 ± 30.94 NPM/100 mL and 16.74 ± 6.61 to 25.43 ± 11.73 NPM/100 mL, respectively. Heavy metals were detected at concentrations between (4 × 10−4) ± (5 × 10−4) mg/L and 0.02 ± (8 × 10−4) mg/L. The water quality index (WQI) indicated poor quality during the dry season (WQI = 30) and moderate quality in the rainy season (WQI = 50). ConclusionsWhile most stations met COD and DO standards during the rainy season, only one met these standards in the dry season. pH levels failed to meet standards in both seasons, and phosphate levels varied. The pollution index classified the river as polluted in the dry season and lightly polluted in the rainy season, highlighting the need for effective pollution management strategies.

Physics-informed neural networks approach for pollutant dispersion flow caused by magnetic dipole

Abstract Research on the liquid flow and distribution of waste discharge concentration over cylindrical surfaces might lead to the growth of efficient waste management and pollution controlling strategies. In view of this, the pollutant concentration and magnetic dipole impact on the liquid flow through the cylinder with a porous medium are explored in the present analysis. Moreover, Fourier’s and Fick’s laws are considered to explore heat transport features. The governing partial differential equations (PDEs) of the present study are converted into non-dimensional ordinary differential equations (ODEs) utilizing similarity variables. Also, the current study proposes a novel implementation of neural networks based on physics knowledge for the liquid flow past a cylinder. The self-updating weights and bias facilitate the network to satisfy the minimization objective function and provide accurate results. Additionally, the solution obtained by Runge Kutta Fehlberg's fourth-fifth order (RKF-45) method is utilized to validate the physics-informed neural network (PINN) results, presenting mean squared errors from 10-9 to 10-11. Furthermore, it is found that the increase in temperature relaxation time parameter decreases the Nusselt number gradually at the rate of 1 to 5%. An upsurge in the values of the pollutant external source parameter and the pollutant external source variation parameter decreases the concentration profile.

PM10 AIR QUALITY INDEX MODELING USING ARFIMA-GARCH METHOD: BUNDARAN HI AREA OF DKI JAKARTA PROVINCE

Air quality is an essential factor in urban life, and its’ assessment often relies on the concentration of measurable air pollution parameters. One critical parameter is Particulate Matter (PM), particularly PM10, which comprises solid or liquid particles dispersed in the air from various sources. One of the methods employed for predicting stock index prices is ARFIMA. ARFIMA is used to model long memory data characterized by a slowly decreasing Autocorrelation Function (ACF) plot (hyperbolic) or a difference value in the fractional from. This method is widely used due to its ability to handle nonstationarity issues in time series. However, the time series data often contain heteroskedasticity problems. Data with heteroscedasticity are then further addressed using the GARCH model, because it can model volatility changes occurring over longer periods and capture the persistence of volatility. The ARFIMA-GARCH model can explain long-memory patterns in time series data and address heteroscedasticity issues. The data are sourced from the Jakarta open data web, which is integrated with DLH DKI Jakarta Province. The aim of this research was to forecast the PM10 air quality index at the Bundaran HI Area in the Province of DKI Jakarta for the next 14 days, from January 1st to January 14th, 2021, using an ARFIMA model enhanced with GARCH. The analysis reveals that the best model is ARFIMA ([17], d, [1])-GARCH (1,1). Forecasting using this model resulted in a MAPE of 3.47%, indicating that the model is highly capable of forecasting several periods.

Implementation of stacking regressor model on the flow induced by TiO2‐H2O and Ti6Al4V‐H2O nanofluid with waste discharge concentration

AbstractThe present investigation examines the circulation of and based nanofluids while considering the concentration of waste discharge. An innovative stacking regressor model is used to increase prediction accuracy. Using Shooting and Runge Kutta Fehlberg's fourth and fifth‐order schemes, the governing equations are converted into ordinary differential equations using similarity transformation and then numerically solved. The findings are represented graphically, and the model's correctness is assessed using Gaussian Process Regression, Categorical Boost, Extreme Gradient Boosting, and Random Forest, with linear regression acting as a meta‐model. The closely related testing and training data show the model's consistency and stability. Magnetic field and inclination angle will decline the velocity, space, and temperature‐dependent internal heat generation factors will enhance the temperature. Raising the pollutant external source parameter raises concentration. In all the cases, shows better performance than based nanofluid. The work's application ranges from fluid dynamics to waste management. By offering precise forecasts of nanofluid concentration, the proposed prediction model may aid in designing and optimizing waste discharge systems.

Enhanced textile wastewater remediation in Phragmites karka-based vertical flow constructed wetlands using Phragmites-derived biochar

Vertical flow-constructed wetlands (VFCWs) are treatment systems that can be used for the phytoremediation of highly polluted textile wastewater. Using plant-derived biochar to simultaneously improve the contaminant removal performance of CWs and sustainable utilization of harvested plant biomass is an interesting proposition. The present study explored the phytoremediation potential of Phragmites karka and verified the impact of using P. karka-derived biochar as a substrate in VFCWs for the treatment of textile wastewater. For this, three types of VFCWs were designed; (i) non-vegetated (VFCW), (ii) vegetated with P. karka (VFCW–P), and (iii) vegetated with P. karka and amended with P. karka-derived biochar (VFCW-BP) and semi-batch experiments were conducted. The investigation confirmed that wetlands using biochar as substrate were more efficient than other wetlands in pollutant load reduction. The maximum pollutant removal efficiencies were recorded for VFCW-BP vis-à-vis COD (83.61%), color (77.87%), chloride (73.22%), calcium (73.52%), sodium (67.18%), and potassium (75.72%) after five days. Furthermore, biochar addition enhanced the growth conditions for wetland plants by alleviating osmotic and oxidative stresses and hence helped them to perform better while removing pollutants. The maximum reduction of various pollutant parameters was reached within 72 h, after which remediation efficiency was slowed down. The study suggests that VFCW with biochar amendment is a useful strategy for textile wastewater treatment. Because the experimental design satisfies the needs for low-cost wastewater treatment, it may find widespread applications.

Application of machine learning and deep learning techniques in modeling the associations between air pollution and meteorological parameters in urban areas of tehran metropolis.

Tehran, the most crowded city in Iran, suffers from severe air pollution, particularly during the cold months. This research endeavored to examine the statistical relationships between criteria air pollutants (CO, NO2, SO2, O3, PM10, and PM2.5) and meteorological elements (temperature, rainfall, wind speed, relative humidity, air pressure, sunshine hours, solar radiation, and cloudiness), as well as assess and compare the efficacy of six different algorithms (multiple linear regression (MLR), generalized additive model (GAM), classification and regression trees (CART), random forest (RF), gradient boosting machine (GBM), and deep learning (DL)) in modeling pollutants and climatic factors responsible for variations in Tehran's air pollution levels from 2001 to 2021 using R 4.3.2 software. The results of this study showed that O3 was strongly affected by weather conditions, while other pollutants were mainly influenced by each other than by meteorological parameters and more extensive research is required to pinpoint the precise impact of human activity on these pollutant levels in Tehran. Also based on the predictive model performance evaluation and concerning the principle of parsimony, in half of the cases, the MLR outperformed other models, despite its seeming simplicity and principal assumptions dependence. In other situations, the GAM was a good substitute.

Derivation of Rating Curve for Suspended Sediment Flow of River Indus at Besham

This study aims at reclaiming the huge amount of wastewater, produced from pulp and paper industry by physico-chemical treatment method following a combination of primary settling, coagulation-flocculation aided clarification (alum, lime and magnesium sulphate as coagulant) and activated carbon adsorption, in order to reduce the quantity of wastewater generation, from this water intensive industry. The chemical secondary treatment indicate the reduction in turbidity (89%), COD (84%), Total Suspended Solids (90%) and colour (89%) was obtained at the mass loading of 3400 mg l-1 of MgSO4, when 4 hours primary treated effluent was further treated by coagulation flocculation process. The combination of primary settling and lime coagulation (optimum dosage of 1400 mg l-1) resulted in effluent turbidity removal of 94%, COD reduction of 86%, TSS (Total Suspended Solids) removal of 93% and colour removal of 91.6% at initial pH of 11. The results showed that plain settling with 4 hours of retention time was effective for reducing the pollution load form pulp and board mill wastewater and this treatment step could reduce the pollution parameters up to 30%. This water could be recycled back into production process. Pilot scale study and further investigation is required before implementation of proposed treatment technology.

Rancang Bangun Pendeteksi Pencemaran Air Laut Berbasis Microprocessor

Sea water pollution is an increasingly urgent global issue, caused by various factors such as industrial waste, household waste, and ship activities. One of the solutions needed to overcome this pollution is the development of a detection system that is able to monitor polluting substances quickly, accurately, and efficiently. This research aims to design and build a microcontroller-based seawater pollution detection system, which can identify various pollution parameters in real-time. This research uses the Research and Development (R&D) method to develop a system consisting of several main components, including a pH sensor, turbidity sensor, Arduino Uno, GPS module, Raspberry Pi 4, USB camera, LiPo battery, and step-down converter. Each component is tested individually before being integrated into the overall system. The results of testing in a real environment show that the system is able to detect seawater pollution parameters with high accuracy. However, there are some errors in data collection, especially in the camera sensor with a percentage error of 32%, turbidity sensor 20%, and pH sensor 24%. Further improvements and developments were made based on the evaluation results to enhance system performance. The resulting system is considered accurate, reliable and easy to use, making an important contribution to efforts to protect seawater quality and mitigate the negative impacts of pollution on the environment and human health.

Assessment of process water quality (depending on the presence of process water systems) in car wash facilities across Germany and the Netherlands

ABSTRACT There are around 2,500 car wash lines in Germany, consuming around 30 million m3 of drinking water per year. In view of the increasing scarcity of water, this level of consumption could soon be restricted by law, as it is in some countries. There are only a few systematic studies on process water systems in car wash lines in Middle Europe; we therefore carried out a survey to determine process water quality parameters (COD, turbidity, water hardness, pH, conductivity, dissolved O2, and temperature). Data were collected and analysed from 63 different locations in Germany and the Netherlands. In addition, longitudinal parameters were measured for 1 year at a single state-of-the-art site in Germany. The scatter of the observed values was quite considerable. The average COD in water of car wash sites with biological treatment was found to be significantly lower than in car wash sites working with filter treatment. Overall, the deviation of the pollution parameters was higher than expected. Surprisingly, there is hardly any reliable data on freshwater consumption in the car wash facilities. As most of the long-established facilities do not have a modern water treatment system, freshwater consumption is up to 300 L per car washed.

Innovative Approaches in Residential Solar Electricity: Forecasting and Fault Detection Using Machine Learning

Recent advancements in residential solar electricity have revolutionized sustainable development. This paper introduces a methodology leveraging machine learning to forecast solar panels’ power output based on weather and air pollution parameters, along with an automated model for fault detection. Innovations in high-efficiency solar panels and advanced energy storage systems ensure reliable electricity supply. Smart inverters and grid-tied systems enhance energy management. Government incentives and decreasing installation costs have increased solar power accessibility. The proposed methodology, utilizing machine learning techniques, achieved an R-squared value of 0.95 and a Mean Squared Error of 0.02 in forecasting solar panel power output, demonstrating high accuracy in predicting energy production under varying environmental conditions. By improving operational efficiency and anticipating power output, this approach not only reduces carbon footprints but also promotes energy independence, contributing to the global transition towards sustainability.

An Integration Method of UML Diagrams and Arduino UNO for Developing Water Pollution Detection System

Despite the scientific and health development in the world, water pollution can pose a threat to human health, whether the pollutants are biological or chemical. In addition, traditional methods of water pollution detecting require high costs, and take time. Due to the importance of water for human life, drinking, irrigation, or for use in industry etc. Thus, reliable methods for water contamination detecting are a crucial issue.This paper aims to propose an integration method of UML diagrams and Arduino UNO microcontroller for developing water pollution detection system. The proposed method consists of three phases, which are system’s design, system's schematic and system’s architecture. It uses use case diagram and sequence diagram. In addition, the system design consisting of a microcontroller and multiple connected sensors for water pollutant detection, namely pH, turbidity, dissolved solids, temperature, oxidation reduction potential and electrical conductivity. The result of this paper is the production an automatic water pollutant detection prototype using Arduino and UML diagrams to display the water pollution parameters. The variables pH, turbidity, dissolved solids, temperature, oxidation reduction potential and electrical conductivity were chosen because they are the physical and chemical parameters that most influence water quality. The proposed system is characterized by measuring the water quality in real time, low cost and its data is accurate and fast.

Computational analysis of nanoparticles and waste discharge concentration past a rotating sphere with Lorentz forces

Abstract As industries rely more and more on magnetohydrodynamic (MHD) systems for different uses in power, production, and management of the environment, it becomes essential to optimize these operations. The study seeks to improve the effectiveness and productivity of cooling structures, chemical reaction reactors, and contaminant control methods by investigating these intricate interconnections. Because of this, the work scrutinizes the endothermic/exothermic (EN/EX) chemical processes, convective boundary conditions, and pollutant concentration impacts on MHD nanofluid circulation around a rotating sphere. The governing equations based on the above assumptions are reduced into a system of ordinary differential equations and solved numerically with Runge–Kutta Fehlberg’s fourth- and fifth- order schemes. The obtained numerical outcomes from the numerical scheme are presented with the aid of graphs, and the results show that the rate of mass transfer decreases with an increase in the external pollutant local source and solid volume percentage. For changes in the values of the activation energy parameter and solid fraction, the rate of thermal dispersion drops for the EN case and upsurges for the EX case. The concentration profile shows increment with the addition of the external pollutant source variation parameter and local pollutant external source parameter. The outcomes of the present work can be helpful in cooling equipment, developing advanced methods for controlling pollution, environmental management, MHD generators, and various industrial contexts.

Air Pollution and Mortality in India: Investigating the Nexus of Ambient and Household Pollution Across Life Stages.

Air pollution in India is a foremost environmental risk factor that affects human health. This study first investigates the geographical distribution of ambient and household air pollution (HAP) and then examines the associated mortality risk. Data on fine particulate matter (PM2.5) concentration has been extracted from the Greenhouse Gas Air Pollution Interactions and Synergies (GAINS) model. HAP, mortality and socio-demographic data were extracted from the National Family and Health Survey-5, India, 2019-2021. Regression models were applied to see the difference in age-group mortality by different pollution parameters. The districts with PM2.5 concentration above the National Ambient Air Quality Standard (NAAQS) level of 40μg/m3 show a higher risk of neonatal (OR-1.86, CI 1.418-2.433), postneonatal (OR-2.04, CI 1.399-2.971), child (OR-2.19, CI 0.999-4.803) and adult death (OR-1.13, CI 1.060-1.208). The absence of a separate kitchen shows a higher probability of neonatal (OR: 1.18, CI 1.074-1.306) and adult death (OR-1.06, CI 1.027-1.088). The interaction between PM2.5 levels above NAAQS and HAP leads toa substantial rise in mortality observed for neonatal (OR 1.19 CI 1.051-1.337), child (OR 1.17 CI 1.054-1.289), and adult (OR 1.13 CI 1.096-1.168) age groups. This study advocates that there is a strong positive association between ambient and HAP and mortality risk. PM2.5 pollution significantly contributes to the mortality risk in all age groups. Children are more vulnerable to HAP than adults. In India, policymakers should focus on reducing the anthropogenic PM2.5 emission at least to reach the NAAQS, which can substantially reduce disease burden and, more precisely, mortality.

Effectiveness Evaluation of Alternative Technologies Applied in Centralized Domestic Wastewater Treatment Systems

The paper aims to determine the effectiveness of several alternative technologies that have been applied in centralized domestic wastewater management systems, especially in West Java and Banten provinces, in the removal of domestic wastewater pollutant parameters. The study was conducted in SPALD-Ts, including the Anaerobic Baffle Reactor (ABR) system, the Anobic Filter (AF) system, the combined ABR + AF system, and the Extended Aeration. The analysis was performed by sampling at the inlet and outlet points of the system for three consecutive years with sampling times from 2021 - 2023. The results of the research show that the efficiency of pollutant parameter separation based on the best technology in the separation of domestic wastewater pollutant parameters is the SPALDT extended aeration technology, where this technology can separate pollutant parameters up to an average of 88.25%, but in this technology requires more cost and periodic maintenance.

The association between airborne particulate matter (PM2.5) exposure level and primary open-angle glaucoma

The eye is vulnerable to the adverse effects of air pollution. Previous experimental study found that fine particulate matter (PM2.5) had a direct toxic effect on intraocular tissues. However, clinical evidence for the impact of air pollutants exposure on functional and structural changes in glaucoma remains scarce. A total of 120 patients with primary open-angle glaucoma (POAG) who met the inclusion criteria were included in this retrospective study. The standardized ophthalmic examination, such as intraocular pressure (IOP), visual field, optical coherence tomography, and comprehensive physical examination, were performed. The air pollution data, including PM2.5 concentration and air quality index (AQI), were collected. PM2.5 and AQI for the day of the medical examination, as well as one month, and three months before the medical examination date, were investigated. In our results, higher average exposure levels for one-month and three-month, were associated with increased IOP (r=0.229, P=0.013; r=0.204, P=0.028, respectively) and decreased visual field mean sensitivity (MS) (r=-0.212, P=0.037; r=-0.305, P=0.002, respectively). PM2.5 concentrations for the day of the medical examination was not significantly associated with ocular parameters. In multiple linear regression analysis adjusted for demographic and clinical factors, higher PM2.5 exposure for one month was associated with elevated IOP (P=0.040, β=0.173, 95 %CI=0.008–0.337). We also found an association between PM2.5 and MS (one-month exposure: β=-0.160, P=0.029; three-month exposure: β=-0.238, P=0.002). The logistic regression analysis found that three-month average PM2.5 exposure level was significantly associated with the disease severity (β=0.043, P=0.025, 95 %CI=1.005–1.084). In conclusion, this study is the first to investigate the relationship between air pollution and detailed ocular parameters of POAG patients in Shanghai over a three-year period, and to explore the effects of different exposure times of PM2.5 on glaucoma. This study found that PM2.5 exposure was correlated with elevated IOP and decreased MS. The one-month PM2.5 exposure level had the most significant effects on IOP. The three-month PM2.5 exposure level was an independent risk factor for POAG severity. Current evidence suggests there may be an association between PM2.5 exposure and POAG.