Quality Monitoring Research Articles

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.

Seasonal anomaly of particulate matter concentration in an equatorial climate: Evaluating the transboundary impact from neighboring provinces on Padang City, Indonesia.

This study investigated the anomalous seasonal variations in particulate matter (PM) concentrations-specifically PM2.5 and PM10-in Padang City, Indonesia, situated within the Equatorial climate zone. A one-year dataset of half-hourly PM measurements from January to December 2023, collected by the Air Quality Monitoring System (AQMS) managed by the Environmental Agency of West Sumatra (DLH), was utilized. Maps of hotspots and air mass backward trajectories were used to identify possible transboundary emissions affecting Padang City. Despite the region experiencing nearly continuous rainfall, significant elevations in PM levels were observed during the typically drier months of August to October. Specifically, PM2.5 levels peaked at 36.57µg/m3 and PM10 at 39.58µg/m3 in October, significantly higher than in other months and indicating a substantial deviation from the typical expectations for equatorial climates. These results suggest that the high PM concentrations are not solely due to local urban emissions or normal seasonal variations but are also significantly influenced by transboundary smoke from peatland fires and agricultural burning in neighboring provinces such as Bengkulu, Riau, Jambi, and South Sumatra. Backward trajectory analysis further confirmed the substantial impact of regional activities on degradation of air quality in Padang City. The study underscores the need for integrated air quality management that includes both local and transboundary pollution sources. Enhanced monitoring, public engagement, and inter-regional collaboration are emphasized as crucial strategies for mitigating the adverse effects of PM pollution in equatorial regions like Padang City.

Simple and Low-Cost Ground Level Ozone Measurement System Based on Internet of Things

Air quality monitoring in Indonesia is still not optimal owing to the lack of ambient air quality monitoring stations. Air quality in Indonesia is expressed by the air pollution status index (ISPU). Ozone is one of the parameters with the highest intensity at ambient air quality monitoring stations which is one of the parameters of ISPU. This study aims to design an ambient air ozone concentration monitoring device connected to a wireless network using economical components. This system uses the MQ131 sensor, DHT22 sensor, STM32 Blue Pill microcontroller, and Lora module, which is equipped with a power supply and protector, and displays monitoring results that provide an overview and additional information related to the air quality of the monitoring location on the website. The designed monitoring device can record ambient air ozone levels at the monitoring location with an average value of 20.33 μg/m3 with an average percent error of 2.34%. The ISPU value obtained during the measurement was 8, and the ISPU status was categorized as good.

A Portable Electrochemical Biosensor Based on an Amino-Modified Ionic Metal-Organic Framework for the One-Site Detection of Multiple Organophosphorus Pesticides.

Constructing stable, portable sensors and revealing their mechanisms is challenging. Ion metal-organic frameworks (IMOFs) are poised to serve as highly effective electrochemical sensors for detecting organophosphorus pesticides (OPs), leveraging their unique charge properties. In this work, an amino-modified IMOF was constructed and combined with near-field communication (NFC) technology to develop a portable, touchless, and battery-free electrochemical biosensor NH2-IMOF@CS@AChE. -NH2 in NH2-IMOF gives the framework a higher electropositivity compared to IMOF, enhancing the electrostatic attraction with acetylcholinesterase (AChE), which is beneficial for immobilizing AChE. Furthermore, the uncoordinated O atoms and the (CH3)2NH2+ groups in NH2-IMOF help to form stronger bonds with AChE through hydrogen bonds. The results showed a wide linear response range of 1 × 10-15 to 1 × 10-9 M and a low detection limit of 1.24 × 10-13 M for glyphosate (Gly) in the practical detection of OPs. Additionally, electrochemical biosensor arrays were constructed to effectively identify and distinguish multiple OPs on the basis of their unique differential pulse voltammetry (DPV) electrochemical signals. This work provides a simple and effective solution for on-site OP analysis and can be widely applied in food safety and water quality monitoring.

Rapid assessment implementation in the development of biocriteria and organic enrichment evaluation in the Citarum River, Indonesia

ABSTRACT Socio-economic activities along the Citarum River basin in Indonesia can induce ecological disturbances in the structure of the benthic macroinvertebrate community. This research aims to (1) identify the environmental factors responsible (2) describe the ecological disturbances using biological measurements; (3) and to develop local biocriteria using a multi-metric conceptual approach. An inventory of benthic macroinvertebrates was carried out at eight stations over a sampling period of 3 months, along with water quality monitoring. The results confirm the change in the structure of benthic macroinvertebrate communities, explained by seven environmental parameters: dissolved oxygen (DO), ambient habitat quality, turbidity, nutrient enrichment, conductivity, temperature, and embeddedness percentage. The changes were then assessed using six biological measurements. Only four have been shown to be capable as alternatives to existing biocriteria. Their implementation enabled the development of local biocriteria compatible with the multimetric concept known as the cumulative biotic index (CBI).

The First Validation of Aerosol Optical Parameters Retrieved from the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS) and Its Application

In August 2022, China successfully launched the Terrestrial Ecosystem Carbon Inventory Satellite (TECIS). The primary payload of this satellite is an onboard multi-beam lidar system, which is capable of observing aerosol optical parameters on a global scale. This pioneering study used the Fernald forward integration method to retrieve aerosol optical parameters based on the Level 2 data of the TECIS, including the aerosol depolarization ratio, aerosol backscatter coefficient, aerosol extinction coefficient, and aerosol optical depth (AOD). The validation of the TECIS-retrieved aerosol optical parameters was conducted using CALIPSO Level 1 and Level 2 data, with relative errors within 30%. A comparison of the AOD retrieved from the TECIS with the AERONET and MODIS AOD products yielded correlation coefficients greater than 0.7 and 0.6, respectively. The relative error of aerosol optical parameter profiles compared with ground-based measurements for CALIPSO was within 40%. Additionally, the correlation coefficients R2 with MODIS and AERONET AOD were approximately between 0.5 and 0.7, indicating the high accuracy of TECIS retrievals. Utilizing the TECIS retrieval results, combined with ground air quality monitoring data and HYSPLIT outcomes, a typical dust transport event was analyzed from 2 to 7 April 2023. The results indicate that dust was transported from the Taklamakan Desert in Xinjiang, China, to Henan and Anhui provinces, with a gradual decrease in the aerosol depolarization ratio and backscatter coefficient during the transport process, causing varying degrees of pollution in the downstream regions. This research verifies the accuracy of the retrieval algorithm through multi-source data comparison and demonstrates the potential application of the TECIS in the field of aerosol science for the first time. It enables the fine-scale regional monitoring of atmospheric aerosols and provides reliable data support for the three-dimensional distribution of global aerosols and related scientific applications.

Unlocking the learning in formal complaints: the development of an end-of-life care complaint analysis tool

Background/Aims End-of-life care is a targeted area of healthcare service reform. Complaints are increasingly recognised as a valuable source of evidence for evaluating practice and informing service improvement work. This project aimed to develop a tool to support the standardised coding, categorisation and organisational use of formal complaint data concerning end-of-life care for adults. Methods A service evaluation was conducted using existing data. Deductive analysis was conducted with 59 letters of formal complaint submitted to an NHS trust in the Midlands, UK. A published healthcare complaints analysis tool served as a thematic framework for use in conjunction with framework analysis. The project was carried out in two stages: the first comprised preliminary work to produce a template of end-of-life care concerns; the second involved pilot testing the face validity of the template when used as a coding and categorising scheme. Results An initial template of 20 subcategories of end-of-life care concerns were developed from the data during preliminary work. This template was deemed to enable the analyst to focus on predefined areas of interest, organise concerns into relevant themes, concisely present the data in a summarised form and provide meaningful objective conclusions. During pilot testing, 185 complainant concerns were coded and categorised, over half of which fell into the domain of ‘relationship problems’ (n=99; 53.5%). Within this domain, the most common problem categories were ‘communication’ (n=44) and ‘respect and rights’ (n=44). A total of five new subcategory concerns were also derived and added to the template. Collectively, the outcomes of complaint data analysis informed the structure of a dedicated end-of-life care complaint analysis tool comprising 25 subcategories of end-of-life care concerns and accompanying exemplar indicators, together with criteria that were meaningful to identifying the context of the care concerns. Conclusions A systematic approach to analysing and reporting complainants' experiences of end-of-life care has the potential to support organisational learning, quality monitoring and bespoke service improvements in response to collective complaints and concerns.

An uncertainty‐based evaluation of sulphuric compounds transport in reservoirs considering sensitivity analysis

AbstractDeepwater bodies such as dam reservoirs are among the most prominent water resources that supply societies' water demand. Hence, water quality in these resources requires continuous and meticulous monitoring. This study evaluated the water quality of Changuleh Dam, a dam under construction in western Iran, using the CE‐QUAL‐W2 model to predict fluctuations in sulphate (SO42−) and hydrogen sulphide (H2S) concentrations. A sensitivity analysis was conducted to assess the dependence of model outputs on input variables. Additionally, an uncertainty analysis was performed using MATLAB to address the inherent uncertainties of the input variables through an ARIMA (2, 2, 1) time series model and Monte Carlo simulations. The results indicated a high sulphate concentration averaging 955.6 mg/l, considerably exceeding the drinking water standard of 400 mg/l, identifying the reservoir as a sulphate‐rich water resource. Seasonal fluctuations of H2S concentrations were also observed in the reservoir's anaerobic bed sediments, peaking at 87 mg/l. The sensitivity analysis showed that nitrate and total dissolved solids (TDS) varied more noticeably than dissolved oxygen (DO) at different depths, with the greatest variation at 50 m. This study highlights the critical need for continuous and meticulous monitoring of water quality in dam reservoirs to meet water supply standards.

A probabilistic framework for identifying anomalies in urban air quality data.

Just as the value of crude oil is unlocked through refining, the true potential of air quality data is realized through systematic processing, analysis, and application. This refined data is critical for making informed decisions that may protect health and the environment. Perhaps ground-based air quality monitoring data often face quality control issues, notably outliers. The outliers in air quality data are reported as error and event-based. The error-based outliers are due to instrument failure, self-calibration, sensor drift over time, and the event based focused on the sudden change in meteorological conditions. The event-based outliers are meaningful while error-based outliers are noise that needs to be eliminated and replaced post-detection. In this study, we address error-based outlier detection in air quality data, particularly targeting particulate pollutants (PM2.5 and PM10) across various monitoring sites in Delhi. Our research specifically examines data from sites with less than 5% missing values and identifies four distinct types of error-based outliers: extreme values due to measurement errors, consecutive constant readings and low variance due to instrument malfunction, periodic outliers from self-calibration exceptions, and anomalies in the PM2.5/PM10 ratio indicative of issues with the instruments' dryer unit. We developed a robust methodology for outlier detection by fitting a non-linear filter to the data, calculating residuals between observed and predicted values, and then assessing these residuals using a standardized Z-score to determine their probability. Outliers are flagged based on a probability threshold established through sensitivity testing. This approach helps distinguish normal data points from suspicious ones, ensuring the refined quality of data necessary for accurate air quality modeling. This method is essential for improving the reliability of statistical and machine learning models that depend on high-quality environmental data.

Smart paper-based materials incorporating nitrogen and boron co-doped MXene quantum dots for rapid adsorption and sensitive detection of Cr2O72−

Dichromate ion (Cr2O72−) is a highly toxic chromium-containing compound that poses significant hazards to the digestive, respiratory systems, skin, and mucous membranes. Currently, the detection and adsorption of Cr2O72− face significant challenges, including the time-consuming and low sensitivity nature of traditional analytical methods. The limited efficiency and capacity of existing adsorbents hinder their practical application in real-time water quality monitoring and environmental remediation. Herein, using polyethyleneimine-functionalized (PEI) pulp fiber paper as the substrate, we developed smart paper-based materials (designated as NB-MQDs@PP) incorporated with nitrogen and boron co-doped MXene quantum dots (NB-MQDs) for rapid adsorption and sensitive detection of Cr2O72−. Compared to undoped MQDs, NB-MQDs exhibited longer excitation wavelength and enhanced oxidation stability. As anticipated, NB-MQDs achieved rapid (response time of 10 s) and sensitive (detection limit of 1.2 μM) recognition of Cr2O72− within a wide pH range with a quenching efficiency of 99.9%. Simultaneously, two on-site detection methods, immersion and cyclic filtration, were constructed based on NB-MQDs@PP. The detection limit of the immersion method was 17.0 nM, while the cyclic filtration method had a detection limit as low as 3.8 nM, surpassing the majority of those reported literatures. Remarkably, NB-MQDs@PP exhibited outstanding enrichment capacity towards Cr2O72−, with an adsorption capacity of up to 162.4 mg/g. This work provides a novel strategy for creating unique paper-based materials with excellent capture and monitoring dual-function, which can be customized according to the requirements of various application scenarios.

AUTOMATIC PANEL QUALITY MONITORING SYSTEM USING VISION-BASED ABNORMAL OBJECT DETECTION

The rate of “Not Good” products due to the presence of abnormal objects during the quality control inspection of mobile phone’s panels is as high as 38 percent without supervision and 19 percent with naked eye’s inspection. To reduce the loss of small abnormal objects and improve the recognition rate of object detection of automatic panel quality monitoring, the paper proposes abnormal object recognition based on a template matching and subtract background technique. Firstly, the object recognition network based on convolutional neural networks (CNNs) is introduced initially. Then, the Canny Edge Detection method improves the image input quality. Next, template matching is utilized to discover things that depart from a standard pattern or a predetermined model. Models for background reduction will be implemented instantly into smartphone panel quality checking systems. Continually, a vision-based quality control (QC) strategy will be developed. Lastly, experimental results from a practical vision-based automatic anomalous object identification system demonstrate the viability and enhancement of the suggested strategy.

Evaluation of Autonomous Navigation and Path Accuracy for Unmanned Surface Vehicles Using IMU Sensor and GPS Data

The development of autonomous systems for environmental monitoring is becoming increasingly important. This project focuses on creating a robotic marine vessel, Aqua Sense, for efficient water quality monitoring. Existing methods are often labour-intensive, time-consuming, and lack real-time data capability. The objective is to develop a robotic vessel with autonomous navigation for water quality applications. A comprehensive design approach, including a literature review and careful component selection, was employed. Key components include the Arduino Mega, Blue Robotics T200 thruster, GlobalSat BU-353N5 GPS sensor, BNO055 Inertial Measurement Unit (IMU), and a Li-po 11.1v battery. In this paper, several field trials data collection is presented to evaluate the navigation algorithm. These findings validate the feasibility of Aqua Sense for real-world applications. The varying distance errors at points B and C demonstrate an error range, with a maximum error of approximately 1.130m at point B (about 0.11% of the target location) and a significant variation at point C, reaching up to 1.660m (approximately 0.15%).

Deciphering laser shock peening quality monitoring: Wavelet-driven network with interpretability

Quality monitoring of laser shock peening based on acoustic emission technology is a topical multidisciplinary issue that has received much attention in recent years. To acquire complex time-varying information in acoustic emission signals, convolutional neural networks with powerful learning capabilities have shown potential for a wide range of applications. However, the black-box property of the network imposes limitations on its further development and decision credibility. Therefore, this study proposes a wavelet-driven network with theoretical basis and physical significance. This network can cyclically utilize discrete wavelet packet transform to map input features to the wavelet domain during the learning process, thereby obtaining more robust and valuable information. This paper also constructs a novel wavelet attention mechanism that takes into account the difference between low-frequency and high-frequency information, and is able to allocate resources in both the decomposition component and the time-domain dimension. The proposed method can be seen as a multiresolution analysis technique that combines existing physical knowledge with nonlinear feature processing and feature selective enhancement. The results of the two laser shock peening cases show that the proposed method not only outperforms current state-of-the-art models in terms of monitoring performance, but also has better physical interpretability. Importantly, the proposed method has the potential to be further extended to other interpretable structural health monitoring.

Variability of bio-optical properties of Sundarbans mangrove estuarine ecosystem using elemental analysis, Sentinel 3 OLCI imageries and neural network models

Coastal water bodies face significant contamination risks stemming from various factors, such as growing populations, rapid urban development, dam construction, heightened industrial operations (notably in ports), and increasing tourism activities. The release of untreated sewage from municipalities, industrial discharges, and various recreational and commercial activities along the coast significantly deteriorates water quality and presents a serious risk to marine ecosystems, food chains, and human health. Hence, continual monitoring of coastal water quality is essential for the ecological sustainability of marine and aquatic ecosystems. This study examines the spatiotemporal variability of optical characteristics within the Sundarbans estuary system, a transboundary ecosystem shared by India and Bangladesh. The study employs Sentinel-3 OLCI satellite data alongside in-situ measurements of critical water quality parameters, including chlorophyll-a (Chl-a), total suspended matter (TSM), and chromophoric dissolved organic matter (CDOM), collected from 100 sampling sites across the Sundarbans estuarine system during the pre-monsoon, monsoon, and post-monsoon periods of 2021 and 2022.This study employs the sophisticated C2RCC model, a machine learning-based neural network-driven approach recognized for its robustness and precision. It examines the various bio-optical properties in tropical estuarine environments. The C2RCC model addresses this challenge directly. This advanced processor, powered by neural network technology, effectively interprets the complex optical signals found in Case-2 waters, offering essential insights into their quality. The findings indicate the highest TSM levels during the pre-monsoon seasons of both years. Additionally, the estimated Chl-a concentrations indicated a rise during the monsoon season (0.03–4 mg m3), whereas the intensified pre-monsoon CDOM levels showed consistency across both years. The strong positive correlation observed between in-situ and satellite data, with an average R2 value ranging from 0.90 to 0.99, underscores the reliability of the high-resolution remote sensing approach. The study illustrates the efficacy of OLCI data in observing near-shore and coastal ecosystems, clearly exceeding conventional methods for evaluating Chl-a, TSM, and CDOM. This study underscores the potential of high spatial and spectral resolution of Sentinel-3 Ocean color data for effective monitoring and management of the complex estuarine ecosystem of the Sundarbans. The findings of this study provide important insights to inform the development of sustainable coastal water resource management strategies. Furthermore, the findings will be crucial in achieving Sustainable Development Goal 14, which focuses on life below water, particularly in the conservation and management of marine and coastal biodiversity.

Integrated water quality assessment of open water bodies using empirical equations and remote sensing techniques in Bangweulu Wetland lakes, Zambia

Water quality assessment is essential for conserving aquatic ecosystems and human consumption. However, monitoring several water quality parameters in open water bodies poses significant challenges, resulting in gaps and limitations. While the Water Quality Index (WQI) integrates various water quality parameters, its spatial application has not yet been thoroughly evaluated. To address these issues, this study was conducted to establish an integrated remote sensing approach for comprehensive monitoring of water quality. Utilising Sentinel 2 data alongside on-site/lab water quality measurements, various relationships were established. The calculated mean WQI from on-site/lab data was 35, while from remotely sensed data was 41. Linear relationships (R2) between WQI and various water quality parameters such as chloride (0.712), sodium (0.705), TDS (0.705), turbidity (0.628), and EC (0.704) were observed. These relationships facilitated the transformation of water quality parameter maps into WQI maps, the WQI maps were then integrated into one WQI map showing the overall water quality status of the Bangweulu Wetland lakes. Both on-site/lab and remote sensing methods indicated that the concentrations of water quality parameters in the Bangweulu Wetland lakes is lower (better) than the local and international recommended limits, with a calculated WQI falling within the ’Good’ category. This indicates that the water is fresh, clean, and suitable for various purposes including ecological preservation, agriculture, aquaculture, recreation, industrial use, and human consumption. The findings offer insights into the general status of water quality in open water bodies, identifying hot spots and potential sources of water pollution with implications for future management practices.

Inter-annual changes in transboundary air quality from KORUS-AQ 2016 to SIJAQ 2022 campaign periods and assessment of emission reduction strategies in Northeast Asia

Northeast Asia suffers from high concentrations of particulate matter (PM), prompting nations to actively implement emission reduction policies. This study evaluated the recent inter-annual changes in the chemical transformation and transboundary transport of PM over Northeast Asia, based on both ground and aircraft measurements, as well as WRF-Chem simulations, during two comprehensive campaigns: the Korea–United States Air Quality (KORUS-AQ) campaign in 2016 and the Satellite Integrated Joint Monitoring of Air Quality (SIJAQ) campaign in 2022, both conducted around the Korean Peninsula. Ground measurements in 2022 revealed significant reductions in air pollutants compared to 2016 levels. In the Beijing–Tianjin–Hebei region, PM2.5 and SO2 concentrations decreased by 47.2% and 73.9%, respectively, attributable to successful SOx and NOx emission reduction strategies. Similar trends were observed in downwind areas, including Seoul, where PM2.5 and SO2 levels declined by 30.0% and 41.4%, respectively. WRF-Chem model results indicated substantial decreases in sulfates, nitrates, and their precursors in both surface and upper atmosphere in 2022 compared to 2016. Moreover, model-calculated gas-to-particle conversion ratios, which peaked in the Yellow Sea in 2016, decreased by 15% in 2022 and shifted slightly eastward to the western Korean Peninsula. This shift suggests a potential decline in secondary PM formation processed in the Yellow Sea, coinciding with reduced long-range transport of gaseous pollutants. A comparison of model sensitivity experiments, accounting for both bottom-up emission changes and meteorological variations, revealed that while weather and climate factors such as precipitation and pressure patterns between 2016 and 2022 contributed to the overall decrease in PM concentrations, the primary driver was the reduction in emissions during this period. This study highlighted that the main driver of the substantial improvement in air quality over East Asia was the implementation of emission reduction policies targeting PM and its precursors in the main source regions in China.

Simulation Modelling of Cycle Chemistry Monitoring of Water and Steam Quality at Thermal Power Plants

Simulation Modelling of Cycle Chemistry Monitoring of Water and Steam Quality at Thermal Power Plants

Remote sensing monitoring of ecological environment quality in mining areas under the perspective of ecological engineering

Remote sensing monitoring of ecological environment quality in mining areas under the perspective of ecological engineering

Innovative lake pollution profiling: unveiling pollutant sources through advanced multivariate clustering techniques.

In many developed and developing nations, lakes are the primary source of drinking water. In the current scenario, due to rapid mobilization in anthropogenic activities, lakes are becoming increasingly contaminated. Such practices not only destroy lake ecosystems but also jeopardize human health through water-borne diseases. This study employs advanced hierarchical clustering through multivariate analysis to establish a novel method for concurrently identifying significantly polluted lakes and critical pollutants. A systematic approach has been devised to generate rotating component matrices, dendrograms, monoplots, and biplots by combining R-mode and Q-mode analyses. This enables the identification of contaminant sources and their grouping. A case study analyzing five lakes in Bengaluru, India, has been conducted to demonstrate the effectiveness of the proposed methodology. Additionally, one pristine lake from Jammu & Kashmir, India, has been included to validate the findings from the aforementioned five lakes. The study explored correlations among various physical, chemical, and biological characteristics such as temperature, pH, dissolved oxygen, conductivity, nitrates, biological oxygen demand (BOD), fecal coliform (FC), and total coliform (TC). Critical contaminants forming clusters included conductivity, nitrates, BOD, TC, and FC. Factor analysis identified four primary components that collectively accounted for 85% of the overall variance. Following identification of pollution hotspots, the study recommends source-based pollution control and integrated watershed management, which could significantly reduce lake pollution levels. Continuous monitoring of lake water quality is essential for identifying actual contaminant sources. These findings provide practical recommendations for maximizing restoration efforts, enforcing regulations on pollutant sources, and improving water quality conditions to ensure sustainable development of lakes.

Enhancing infection control in dialysis at a resource limited public healthcare institute: A cross-sectional study on microbiological quality assessment of dialysis water and dialysate

PurposeTo evaluate the microbiological quality of dialysis water and dialysate samples from hemodialysis units at a tertiary care government hospital and to assess the use of culture, endotoxin and periodicity of testing these assays to guide the actions of monitoring the quality of hemodialysis fluids, implement preventive and corrective actions, and improve the safety and outcomes of the dialysis process. MethodsA cross-sectional study was conducted at a 250-bedded super-specialty government hospital with a 24/7 hemodialysis unit equipped with 40 dialysis machines. Dialysis water and dialysate samples were collected monthly and analysed for microbial contamination and endotoxin levels as per AAMI guidelines. Bacterial cultures were done using Reasoner's Agar plates, and endotoxin analysis was performed using gel clot assay. Interpretation of results was based on predefined thresholds. ResultsAmong the 740 samples processed for microbial culture 19.6 % and 80.4% were unacceptable and acceptable respectively.Among the acceptable samples 10.5 % were at action level. At the end of 2 days of incubation, 15.2%dialysis water samples and 5.1%dialysate samples had unacceptable levels of bacterial colonies and at 5days additional 10.6 % dialysis water samples and 7.7%dialysate samples were unacceptable. 21.5 % of the samples tested for endotoxin had unacceptable levels. ConclusionsRegular monitoring of water quality in government healthcare hemodialysis units is crucial for quality of care, timely preventive and corrective actions for mitigating adverse outcomes. Processing cultures for 5–7 days is essential for detecting all contaminated specimens. While there might be a link between endotoxin levels and bacterial contamination, both endotoxin testing and bacterial culture independently are vital for evaluating water quality in dialysis settings. A comprehensive approach integrating various testing methods is necessary to uphold patient safety and enable necessary improvements.