Operation Of Wastewater Treatment Plants Research Articles

Alternatives to 137Cs for 210Pb dating validation in south America and in the Carribean region: Point and diffuse sources

As today the 137Cs fallout peak, in sediment cores, corresponds only to 25% of its initial concentration, alternatives to the use of 137Cs as a210Pb sediment dating validation tool are proposed. In highly industrialized bays, such as Guanabara Bay in the Rio de Janeiro metropolitan region, several chemical/compounds from the surrounding industry releases may be applied as validation tools. Chromium and copper profiles in a sediment core adequately fit the expected pattern due to the implementation of a chemical plant in 1958, reaching their maximum discharge in 1982 and subsequently decreasing due to the operation of a new wastewater treatment plant. A diffuse source such, as the PCB-based mixture Askarel, was also applied as an alternative validation tool, and the observed concentration profile reproduced the expected behavior, with increasing concentration after the Second World War and a decrease after the ban of this product in 1981. The observed Aroclor 1254 and 1260 mixture chlorination rates fit the most widely distributed PCB-based products in the country.

Identification of illicit discharges in sewer networks by an SWMM-Bayesian coupled approach.

Illicit discharges into sewer systems are a widespread concern within China's urban drainage management. They can result in unforeseen environmental contamination and deterioration in the performance of wastewater treatment plants. Consequently, pinpointing the origin of unauthorized discharges in the sewer network is crucial. This study aims to evaluate an integrative method that employs numerical modeling and statistical analysis to determine the locations and characteristics of illicit discharges. The Storm Water Management Model (SWMM) was employed to track water quality variations within the sewer network and examine the concentration profiles of exogenous pollutants under a range of scenarios. The identification technique employed Bayesian inference fused with the Markov chain Monte Carlo sampling method, enabling the estimation of probability distributions for the position of the suspected source, the discharge magnitude, and the commencement of the event. Specifically, the cases involving continuous release and multiple sources were examined. For single-point source identification, where all three parameters are unknown, concentration profiles from two monitoring sites in the path of pollutant transport and dispersion are necessary and sufficient to characterize the pollution source. For the identification of multiple sources, the proposed SWMM-Bayesian strategy with improved sampling is applied, which significantly improves the accuracy.

Development of an artificial neural network (ANN) for the prediction of a pilot scale mobile wastewater treatment plant performance

Productive activities such as pig farming are a fundamental part of the economy in Mexico. Unfortunately, because of this activity, large quantities of wastewater are generated that have a negative impact in the environment. This work shows an alternative for treating piggery wastewater based on advanced oxidation processes (Fenton and solar photo Fenton, SPF) that have been probed successfully in previous works. In the first stage, Fenton and SPF were carried out on a laboratory scale using a Taguchi L9-type experimental design. From the statistical analysis of this design, the operating parameters: pH, time, hydrogen peroxide concentration [H2O2], and iron ferrous concentration [Fe2+] that maximize the response variables: Chemical Oxygen Demand (COD), Total Organic Carbon (TOC), and color were chosen. From these, a cascade forward neural network was implemented to establish a correlation between data from the variables to the physicochemical parameters to be measure being that a great fit of the data was obtained having a correlation coefficient of 0.99 which permits to optimize the pollutant degradation and predict the removal efficiencies at pilot scale but with a projection to a future industrial scale. A relevant result, it was found that the optimal values for maximizing the removal of physicochemical parameters were pH = 3, time = 60 min, H2O2/COD = 1.5 mg L−1, and H2O2/Fe2+ = 2.5 mg L−1. With these conditions degradation percentages of 91.44%, 47.14%, and 97.89% for COD, TOC, and color were obtained from the Fenton process, while for SPF the degradation percentage increased moderately. From the ANN analysis, the possibility to establish an intelligent system that permits to predict multiple results from operational conditions has been achieved.

Integrated environmental management and GPS-X modelling for current and future sustainable wastewater treatment: A case study from the Middle East

In the context of today's rapidly changing environmental challenges, accurately predicting the performance and efficiency of environmental management strategies is crucial. Particularly in the Middle East, where research on wastewater treatment plants (WWTPs) is notably lacking, addressing this need is imperative. This study investigates the treatment efficiency of a wastewater treatment plant and proposes various techniques to enhance its performance. Employing a case study method, we utilise the GPS-X model to forecast the plant's performance under diverse scenarios, offering solutions for future challenges. The results reveal that the current plant layout operates efficiently, with removal efficiencies for Total Suspended Solids (TSS), Chemical Oxygen Demand (COD), and Biochemical Oxygen Demand (BOD) at 98.3 %, 95.1 %, and 96.1 %, respectively. The outlet Dissolved Oxygen (DO) of 1.9 mg/L meets local wastewater reuse standards. Furthermore, the GPS-X model forecasts the plant's performance under different scenarios, suggesting the feasibility of a new layout within 20–25 years and the need for additional units after 40 years. As inflow approaches maximum design capacity, simulation results underscore the importance of utilising the full plant design and expanding it for optimal operation over 60 years. This research provides critical insights for improving WWTP performance and emphasizes the significance of strategic planning in addressing long-term environmental management challenges. Moreover, this study represents a pioneering effort in addressing critical water scarcity challenges in Jordan by exploring the potential of treated wastewater (TWW) as a sustainable solution, thus contributing to the advancement of environmental management practices in the region.

Novel extended hybrid tool for real time control and practically support decisions to reduce GHG emissions in full scale wastewater treatment plants

In this paper, a novel methodology and extended hybrid model for the real time control, prediction and reduction of direct emissions of greenhouse gases (GHGs) from wastewater treatment plants (WWTPs) is proposed to overcome the lack of long-term data availability in several full-scale case studies. A mechanistic model (MCM) and a machine learning (ML) model are combined to real time control, predict the emissions of nitrous oxide (N2O) and carbon dioxide (CO2) as well as effluent quality (COD – chemical oxygen demand, NH4-N – ammonia, NO3-N - nitrate) in activated sludge method. For methane (CH4), using the MCM model, predictions are performed on the input data (VFA, CODs for aerobic and anaerobic compartments) to the MLM model. Additionally, scenarios were analyzed to assess and reduce the GHGs emissions related to the biological processes. A real WWTP, with a population equivalent (PE) of 125,000, was studied for the validation of the hybrid model. A global sensitivity analysis (GSA) of the MCM and a ML model were implemented to assess GHGs emission mechanisms the biological reactor. Finally, an early warning tool for the prediction of GHGs errors was implemented to assess the accuracy and the reliability of the proposed algorithm. The results could support the wastewater treatment plant operators to evaluate possible mitigation scenarios (MS) that can reduce direct GHG emissions from WWTPs by up to 21%, while maintaining the final quality standard of the treated effluent.

Pengaruh Penerapan Sistem IPAL terhadap Kulitas Air Buangan Rumah Pemotongan Hewan

PT X is a company engaged in animal slaughtering and produces liquid waste in the process of its activities. Located in Banyuwangi Regency, East Java Province, this study aims to determine the effect of the implementation of the Wastewater Treatment Plant (WWTP) system on the quality of PT X's wastewater. The effect of implementing the WWTP system can be measured through baseline data collection, evaluation of WWTP effectiveness, observation of WWTP operation and maintenance, and recommendations in accordance with the Regulation of the Minister of Environment and Forestry of the Republic of Indonesia Number 68 of 2016 concerning Domestic Wastewater Quality Standards. Pollutant parameters in wastewater include BOD, COD, TSS, and O&G (Oil & Grease) that exceed the quality standards. After the implementation of the WWTP system, the effluent results showed a decrease and no longer exceeded the quality standards, proving that the implemented WWTP system has a positive effect on the quality of the PT X abattoir discharge water.

A comprehensive comparison of microbial communities between aerobic granular sludge and flocculent sludge for nutrient removal in full-scale wastewater treatment plants

Understanding the microbial community structure of sludge is crucial for improving the design, operation and optimisation of full-scale wastewater treatment plants (WWTPs). This study aimed to have a comprehensive comparison of microbial communities between aerobic granular sludge and flocculent sludge from two full-scale sequential batch reactors-based WWTPs with nutrient removal for the first time. To better understand key functional bacteria such as polyphosphate accumulating bacteria (PAOs), competitive bacteria such as glycogen accumulating bacteria (GAOs) and nitrifying bacteria for both nitrogen and phosphorus removal, another two full-scale WWTPs with only carbon (C) removal and C and nitrogen (N) removal were compared too. It was found that the richness and diversity of the microbial population in sludge increased with pollutant removal from only C, C and N, to C,N, P removal. For C, N P removal, granule structure led to a more diverse and rich microbial community structure than flocculent structure. Although more abundant nitrifying bacteria were enriched in granular sludge than flocculent sludge, the abundance of total putative PAOs was equivalent. However, the most typical putative PAOs such as Tetrasphaera and Candidatus Accumulibacter seemed to be more correlated with biological phosphorus removal performance, which might be more proper to be used as an indication for P removal potential. The higher abundance of GAOs in flocculent sludge with better phosphorus removal performance might suggest that further investigation is needed to understand the functions of GAOs. In addition, the equivalent abundances of PAOs in the WWTPs with only C removal and with C, N, and P removal, respectively, indicate that many newly reported putative PAOs might not contribute to P removal. This study provides insight into the microbial communities and functional bacteria in aerobic granular sludge and flocculent sludge in full-scale SBRs, which can provide microbes-informed optimisation of reactor operation for better nutrient removal.

Watching the guards: A data-driven method to trigger warnings in national wastewater surveillance networks

ABSTRACT Surveillance networks have been established in many countries worldwide to monitor SARS-CoV-2 in sewage and to estimate the communal prevalence of COVID-19 cases. Despite their popularity, gaining a rapid understanding of how infectious diseases spread across the territory covered by a network is difficult because of the many factors involved. To improve the detection of warning signals within the territory, we propose to apply principal component analysis (PCA) to screen time-series data generated from wastewater treatment plants (WWTPs) under surveillance. Our analysis allows us to identify single WWTPs deviating from the normal behavior as well as deviations of a cluster of WWTPs (indicative of an intermunicipal outbreak). Our approach is illustrated through the analysis of the dataset generated by the Catalan Surveillance Network of SARS-CoV-2 in Sewage (SARSAIGUA). Using 10 principal components, we captured 78.6% of the variance in the original dataset of 51 variables (WWTPs). Our analysis identified exceedance of the Q-statistic threshold as evidence of anomalous performance of a single WWTP, and exceedance of the T2-statistic as a sign of an intermunicipal outbreak. Our approach provides a comprehensive picture of the spread of the COVID-19 pandemic, enabling decision-makers to make informed decisions and better manage future pandemics.

Prediction of wastewater treatment plant performance through machine learning techniques

This study investigates the use of developed machine learning techniques for modeling the performance of AlHayer, Saudi Arabia, wastewater treatment plant (ALWTP). Three physio-chemical characteristics were measured and predicted, including chemical oxygen demand (COD), biological oxygen demand (BOD), and suspended solids (SS), at ALWTP. The pre-evaluation of collected data revealed the effective capabilities of ALWTP for the removal of suspended solids, organic, and nutrient pollutants. To estimate the physio-chemical characteristics of ALWATP, four developed machine learning techniques were evaluated and compared. Logistic regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR) were designed. The evaluation of the proposed models showed RF outperformed other proposed models for estimating COD and SS with accuracy 91 % and 95 % in terms coefficient of determination (R2); however, GB was found the best, with accuracy 92 %, for detecting the BOD performance of ALWATP. This indicates ensemble learning models, RF and GB, can be considered a superiority soft solution for estimating physio-chemical characteristics of wastewater treatment plant.

Optimization of Gambier Industry Wastewater Treatment Plant (WWTP) Performance: a Systematic Approach for Environmental Sustainability

Optimization of Gambier Industry Wastewater Treatment Plant (WWTP) Performance: a Systematic Approach for Environmental Sustainability

Performance of wastewater treatment plants in emission of greenhouse gases

The present work has estimated greenhouse gas emissions in aerobic and anaerobic Wastewater Treatment Plants in Southern Italy. Greenhouse gases emissions from each treatment unit were calculated based on emission factors related to Chemical Oxygen Demand removal for biogenic CO2 and CH4 assessment and on Nitrogen removal for N2O. N2O, biogenic CO2, and CH4 emissions vary for aerobic and anaerobic-based WWTPs respectively from 73 kgCO2eq/PE*y for anaerobic plants to 91 kgCO2eq/PE*y for aerobic plants. In aerobic and anaerobic digestion systems WWTPs the contributions to CO2eq total emissions from N2O, CH4, biogenic CO2, and fossil CO2 are 30 %–33 %, 20 %–29 %, 22 %–25 %, and 26 %–16 %, respectively. N2O emissions from biological processes were found the most contributing sources of greenhouse gases while in the physical processes higher contribution is indirect carbon dioxide related to energy consumption. Compensatory measures are reported to reduce greenhouse gases emissions.

Wastewater Treatment Plants Performance for Reuse: Evaluation of Bacterial and Viral Risks

Reusing reclaimed water is of paramount importance to achieve the 2030 Agenda for Sustainable Development Goals 6 and 13. In Europe, a recent Regulation set minimum requirements for water reuse in agriculture. However, some challenges remain considering microbial risks and their prevention. In this study, two urban wastewater treatment plants (WWTPs) were investigated from the perspective of reuse. A five-year investigation was performed on routine monitoring parameters collected under different weather conditions (wet/dry) and treatments (chlorination/non-chlorination) in inlet and outlet samples. Moreover, a three-month investigation focused on microbial parameters, including indicators, index pathogens (Human Adenovirus—HAdV, Salmonella spp.), and other viral pathogens (norovirus, enterovirus, and SARS-CoV-2). The long-term study revealed the compliance of both WWTPs for chemical parameters (organic substances and solids) in more than 90% of samples, whereas for Escherichia coli, the compliance ranged from 96.1% with chlorination under dry weather to 16.7% without chlorination in wet days. E. coli was positively associated with chemical oxygen demand (COD), which could be a promising and online measurable proxy of E. coli. The study on microbial performance demonstrated sound reliability in detecting E. coli as a suitable surrogate for Salmonella in chlorinated effluents, but neither bacterial nor viral indicators are able to represent HAdV. Although chlorination was able to remove most of the pathogens considered, the compliance with microbial indicators seems insufficient to represent viral water safety.

Evaluation of the Performance of Wastewater Treatment Plant at Steel Mill Karachi, Pakistan

The purpose of this study is to evaluate how well Steel Mill Karachi's wastewater treatment system works. Wastewater treatment facilities employ the activated sludge technique. Several influent and effluent locations allowed for the collection of influent and effluent samples. A number of physicochemical parameters, such as total dissolved solids (TDS), biochemical oxygen demand (BOD5), total suspended solids (TSS), chemical oxygen demand (COD), electric conductivity (EC), pH, and other metals, such as potassium (K), iron (Fe), sulphate (SO4-2), calcium (Ca), sodium (Na), zinc (Zn), and magnesium (Mg), were measured in order to evaluate the effectiveness of wastewater treatment plants. In a lab environment, the sample's analysis findings were compared to Sindh Environmental Quality Standards (SEQS) for Pakistan. EC, BOD5, COD, SO4-2, Ca, Mg, Na, K, TDS, TSS, EC, BOD5, COD, SO4-2, Ca, Mg, Na, K, Fe, and Zn were removed to different degrees: 42.93%, 60.33%, 43.63%, 51.53%, 40.5%, 35.56%, 26.33%, 22.12%, 34.09%, 20.75%, 55.87%, 22.12%, and 76.10%. Only two metrics, the COD and Zn concentrations, were over the SEQS limits, keeping other effluent results parameters within the limits. Additionally, it is advised that proper maintenance be carried out to increase the effectiveness of wastewater treatment plants.

EDAR 4.0: Machine Learning and Visual Analytics for Wastewater Management

Wastewater treatment plant (WWTP) operations manage massive amounts of data that can be gathered with new Industry 4.0 technologies such as the Internet of Things and Big Data. These data are critical to allow the wastewater treatment industry to improve its operation, control, and maintenance. However, the data available need to be improved and enriched, partly due to their high dimensionality and low reliability, and the lack of appropriate data analysis and processing tools for such systems. This paper presents a visual analytics-based platform for WWTP that allows users to identify relationships among data through data inspection. The results show that the tool developed and implemented for a full-scale WWTP allows operators to construct machine learning (ML) models for water quality and other water treatment process variables. Consequently, analyzing and optimizing plant operation scenarios can enhance key variables, including energy, reagent consumption, and water quality. This improvement facilitates the development of a more sustainable WWTP, contributing to a beneficial environmental impact. Domain experts validated the variables influencing the created ML models and proved their appropriateness.

Modeling the effects of sidestreams recycling on wastewater treatment plant performance operated by anaerobic-anoxic-oxic (A2/O) processes using GPS-X8 simulation

The water separated by sludge treatment processes is known as the sidestreams, such as the centrate, underflow belt thickener, and supernatant gravity thickener. The sidestreams have significant amounts of contaminants, particularly nutrients, and are returned to the wastewater treatment plant head. Returning the sidestreams to the head of the plant causes both technical and financial problems. This paper models the effect of sidestreams on the treatment of mainstream in full-scale conventional activated sludge systems operated with anaerobic/anoxic/oxic (A2/O). The effects of sidestreams nutrients and inert materials, the pollutants fate in the A2/O system, oxygen consumed, and energy required by sidestreams are modeled using GPS-X8 simulation. The results showed that the discharge ratio of the sidestreams to the mainstream is 4 %. The proportion of the pollutants present in the sidestream to the mainstream were 4.3 %, 4.7 %, 8.8 %, 21 %, 40 %, and 11 % for BOD, COD, TSS, NH4, PO4, and H2S, respectively. The GPS-X model showed that high orthophosphate and hydrogen sulfide concentrations were seen in the anaerobic zone. While all other pollutants begin to decay, phosphates and hydrogen sulfide gas also begin to decrease gradually in anoxic and oxic zones, noting the high concentrations of nitrates and sulfates in the oxic zone. An added 10 % of oxygen is needed for the sidestreams ammonia oxidation, which is more than the amount needed for the oxidation of BOD. The percentage of alkali used to improve the nitrification process due to the ammonia in the sidestreams was 30 % of the specified amount of ammonia in the mainstream. This study proved that the plant's sidestreams adversely increased oxygen, energy, and alkali consumption.

The potential for water recovery from urban waste water - The perspective of urban waste water treatment plant operators in Poland

Water recovery from waste water has become an essential element of the circular economy in the Baltic Sea region. However, there is little data on the possibility of using water recovered from urban waste water. A survey was conducted to learn the opinions of Poland waste water treatment plant operators. They were asked whether they recovered water for internal or external needs. Respondents indicated opportunities and barriers in this activity. The opinions of 107 operators show that work is underway on closing internal circuits in urban WWTPs. These solutions are technically relatively easy to implement and show measurable benefits (i.e., saving drinking water). However, water recovery for external purposes is rare and is at a very early stage. Despite this, the potential is significant, although many financial, organizational, technical, and mental barriers exist. The most critical challenge is the safe use of reclaimed water and the cost-effectiveness of the solutions. The survey also shows a need for education and involvement of the public.

Insightful Analysis and Prediction of SCOD Component Variation in Low-Carbon/Nitrogen-Ratio Domestic Wastewater via Machine Learning

The rapid identification of the amount and characteristics of chemical oxygen demand (COD) in influent water is critical to the operation of wastewater treatment plants (WWTPs), especially for WWTPs in the face of influent water with a low carbon/nitrogen (C/N) ratio. Given that, this study carried out batch kinetic experiments for soluble chemical oxygen demand (SCOD) and nitrogen degradation for three WWTPs and established machine learning (ML) models for the accurate prediction of the variation in SCOD. The results indicate that four different kinds of components were identified via parallel factor (PARAFAC) analysis. C1 (Ex/Em = 235 nm and 275/348 nm, tryptophan-like substances/soluble microbial by-products) contributes to the majority of internal carbon sources for endogenous denitrification, whereas C4 (230 nm and 275/350 nm, tyrosine-like substances) is crucial for readily biodegradable SCOD composition according to the machine learning (ML) models. Furthermore, the gradient boosting decision tree (GBDT) algorithm achieved higher interpretability and generalizability in describing the relationship between SCOD and carbon source components, with an R2 reaching 0.772. A Shapley additive explanations (SHAP) analysis of GBDT models further validated the above result. Undoubtedly, this study provided novel insights into utilizing ML models to predict SCOD through the measurements of the excitation–emission matrix (EEM) in specific Ex and Em positions. The results could help us to identify the degradation and transformation relationship between different kinds of carbon sources and nitrogen species in the wastewater treatment process, and thus provide a novel guidance for the optimized operation of WWTPs.

Effect of Nanobubble Injector–Based Aeration on the Performance of Wastewater Treatment Plant

Effect of Nanobubble Injector–Based Aeration on the Performance of Wastewater Treatment Plant

Using Quantitative Microbial Risk Assessment (QMRA) of SARS-CoV-2 to understand possible exposure to health risks in selected wastewater treatment plants located in the Eastern region of South Africa

In the past two years, Covid-19 has emerged as the most severe and pressing public health issue, causing a great deal of damage to societal and economic welfare, as well as causing illness and mortality. The operators in wastewater treatment plants (WWTPs), particularly those employed in rural communities, appear to often exhibit a lack of adherence to proper safety protocols by not utilizing sufficient protective equipment while handling unprocessed sewage samples throughout the different phases of wastewater treatment and disposal. This study aimed at examining the potential health risk of infection among WWTP operators, as a result of unintentional ingestion of wastewater during routine duties in facilities that receive influent containing Severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) from various areas. This study examined the prevalence of SARS-CoV-2 in grab samples of untreated wastewater samples using the real-time quantitative polymerase chain reaction (RT-qPCR) technique and quantitative microbial risk assessment (QMRA) model was employed on three probable exposure of SARS-CoV-2 scenarios that are expressed as moderate, aggressive and extreme (2 mL, 10 mL, 20 mL) to evaluate the probability of infection to WWTP workers based on the 6 h that the workers spent in WWTPs performing their daily activities which exposed them to potential health risk of various pathogens. At the highest SARS-CoV-2 genome of 266.23 × 102 gc/mL, the findings indicated that there was no statistically significant difference in the probability of infections with respect to seasonal differences because the P(i) value was greater than 0.05 (p > 0.05). Overall, P(i) was highly significant across all volumetric scenarios in the study with p value that was p < 0.001. The probability of getting infected during the different seasons is assumed to be low since there was no statistically difference in P(i) with respect to season however it can be assumed that there is a high chance of getting infected regardless of volumetric intake. Our study suggests that the risk of accidental occupational exposure to SARS-CoV-2 in raw wastewater is negligible to workers whereby workers would perform their daily activities without wearing protective gear. Nevertheless, the importance and work of WWTPs by workers should not be overlooked. Regardless of the situation, it is widely recognized that residential wastewater poses a potential risk of infection due to the presence of several enteric pathogens, therefore, it is crucial to ensure that those who are occupationally exposed to untreated wastewater are well equipped with suitable personal protective equipment (PPE).

Factors affecting the performance of a pharmaceutical wastewater treatment plant: Characterization of effluent and environmental risk

Pharmaceutical industries produce a huge volume of emerging pollutants (EPs) that pose a threat to the aqueous environment. Biological processes have shown their inefficacy in treating many pharmaceutical products. The study assessed physicochemical parameters, EPs, heavy metals in pharmaceutical industrial wastewater, and the removal efficiency (RE) of an aerobic biological treatment plant. The study also assessed the contamination levels and risk using several indices, such as the Canadian Council of Ministers of the Environment Water Quality Index (CCME-WQI), heavy metal pollution index (HPI), heavy metal evaluation index (HEI), and risk quotients index (RQs). The study found that the treated water quality was poor, having antibiotics, nonsteroidal anti-inflammatory drugs, and others, along with several transformation products (TPs) and heavy metals, which were unsafe for consumption with high environmental risk. The analysis results showed that the RE for TSS, BOD5, COD, TDS, and EC were found to be 91.80%, 86.81%, 72.29%, 72.20%, and 65.60%, respectively, where the values of BOD5, COD, NO3−, and PO43− in the effluent were still higher than the permissible limits of the ECR (2023). However, the RE for heavy metals was in the order of Cu (84.62%) > Fe (65.04%) > Mn (63.3%) > Zn (60.58%) > Cd (53.85%) > Ni (54.12%) > Pb (42.42%) > Cr (38%), where Cr and Cd concentrations were still higher than the permissible limit of DoE (2019). The Pearson correlation and PCA suggested that EC, TDS, TSS, DO, BOD5, and COD were the most correlating and contributing variables. This study argued that metal-ligand behaviors mainly affect the removal efficiency of the treatment plant by lowering the removal rate of heavy metals and pharmaceutical products.