Concentration Time Series Data Research Articles

Estimating Air Change Rate in Mechanically Ventilated Classrooms Using a Single CO2 Sensor and Automated Data Segmentation.

With a growing emphasis on indoor air quality (IAQ) in educational environments, CO2 monitoring in classrooms has become commonplace. CO2 data can be used to estimate outdoor air change rate (ACH) based on the mass balance principle, which can be further linked to human health, performance, and building energy consumption. This study used a novel machine learning method to automatically segment CO2 concentration time series data into build-up, equilibrium, and decay periods, and then estimated classroom ACH using the corresponding CO2 mass balance equations. This method, applied to 40 classrooms in two mechanically ventilated K-6 schools, generated up to ten ACH estimates per day per classroom. A comparison with ACH calculated using the mechanical ventilation rates with 100% outdoor air reported by the building automation system during the study period reveals a slight underestimation by the decay and build-up methods, while the equilibrium method produced closer estimates. These differences may be attributed to uncertainties in occupancy, activity, CO2 emission rates, and air mixing. This research underscores the potential of leveraging CO2 data for more comprehensive IAQ assessments and highlights the challenges associated with accurately estimating ACH in real-world settings.

Determination Procedure of Ecotoxicologically Acceptable Cu Concentration in River Considering the Change of Environmental Factors

Objectives : In this study, considering the changes in the concentration of metal/metalloid and environmental factors over time of the target water system and the toxic effects of metals on various aquatic organisms, determined procedure of ecotoxicologically acceptable metal/metalloid concentration based on a biotic ligand model (BLM) was proposed in detail. As an application example, the ecotoxicologically acceptable Cu concentration for the Numedalslågen river in Norway was calculated.Methods : The Cu concentration and environmental factor time series data of the target water system were collected, and EC50[Cu]<sub>T</sub>, the total concentration of Cu in the water system showing a toxic effect of 50% on the target aquatic organisms, was calculated through BLM. In addition, the predicted no-effect concentration (PNEC) was obtained from the species sensitivity distribution using the acute-chronic ratio and adjustment factor, and the ecotoxicologically acceptable Cu concentration of the target water system was determined through the fixed monitoring benchmark (FMB).Results and Discussion : Ecotoxicologically acceptable Cu concentration of the target water system was determined as 1.15 μg/L. Dissolved organic carbon (DOC) showed the highest linear relationship with EC50[Cu]<sub>T</sub> (R<sup>2</sup> = 0.94-0.98) compared to various environmental factors, and the Cu toxicity effect on aquatic organisms tended to decrease as the DOC concentration increased. Ca showed a low linear relationship with EC50[Cu]<sub>T</sub> (R<sup>2</sup> < 0.04), which is presumed to be due to the decrease in Ca<sup>2+</sup> concentration due to the complexese formation of DOC and Ca<sup>2+</sup>, which reduced the competitive effect with Cu<sup>2+</sup>.Conclusion : The proposed procedure provides a useful method for calculating ecotoxicologically acceptable metal concentrations for the protection of aquatic organisms in water systems. In addition, it is expected that it can be used to calculate site-specific metal concentrations that can protect aquatic organisms in the target water system by reflecting the timing of sediment resuspension, which can cause rapid changes in the concentration of metals and environmental factors in the water system, and specific time points such as storm and flood seasons.

Forecasting Methane Data Using Multivariate Long Short-Term Memory Neural Networks

Over the past few decades, Alberta has witnessed a remarkable expansion in its oil and gas sector. Unfortunately, this growth has come at a cost, as Alberta has become the fastest-growing source of pollutant emissions in greenhouse gases (GHGs), sulphur emissions, and water pollution in Canada. Among these GHGs, methane stands out as the second most prevalent GHG, possessing a global warming potential ~ 28 times higher than carbon dioxide over a span of 100 years, and ~ 80 times higher over a period of 20 years. Since 1986, the Alberta Energy Regulator (AER) has been diligently gathering data on methane concentrations. Although this data is publicly available, its analysis has not been thoroughly explored. Our study aims to investigate the impact of temperature, wind speed, and wind direction on the predictions of methane concentration time series data, utilizing a long short-term memory (LSTM) neural network model. Our findings indicate that the inclusion of climate variables enhances the predictive capabilities of the LSTM model. However, the results show that it is not obvious which variable has the most impact on the improvement although temperature appears to have a better effect on improving predictive performance compared to wind speed and direction. The results also suggest that the variance of the input data does not affect forecasting performance.

OpenToolFlux: An Open-Source Software for Estimating Gas Fluxes from Automatic Chamber Data

OpenToolFlux 1.0 is an open-source software designed to estimate soil gas fluxes from gas concentration time-series data generated by automatic chamber systems. This paper describes the physical equipment used as well as the software design and workflow. The software is a command-line application that imports tabular time-series data from the analyzer following the instructions specified in a configuration file by the user, performs configurable data-cleaning operations, and outputs a data file with volumetric flux estimates as well as diagnostic plots. The software can be configured according to the specifics of the physical equipment and experimental setups, and it is, therefore, applicable to a wide range of studies.

Disentangling the impact of COVID-19 lockdown and meteorological factors on air quality in Colombo, Sri Lanka: A data clustering approach

Introduction: Human activities disrupted by COVID-19 have reduced global air pollution. Meteorological day-to-day and year-to-year variability affects pollution levels and complicates estimating reductions. This paper uses data clustering to remove the complexity of non-linear relationships by separating meteorology from complex emission patterns before modelling. The case study is based on PM2.5 concentration time series data and meteorological data for 2018 to 2021 in Colombo, Sri Lanka.
 Materials and methods: The southwest monsoon brings sea breezes from the Indian Ocean to land from May to October. To separate the effect of the monsoon winds on PM2.5 concentrations, analysis of time series data, polar plots, clusters, and Theil-Sen trends were used based on hourly-average air pollution and meteorological data for the whole dataset.
 Results: Two clear clusters were identified from scatterplots, representing the monsoon and non-monsoon periods. The study suggests that due to the combined effect of the monsoon winds and a reduction in the levels of traffic as a result of perturbations in human activity, the PM2.5 concentrations decreased at an average rate of 10.61 µg/m3/year (95% CI: 12.86 - 8.11) over the four years. During the non-monsoon season, due to traffic reductions alone, PM2.5 concentrations reduced at an average rate of 7.95 µg/m /year (95% CI: 10.07 – 5.51).
 Conclusion: These results are relevant to policymakers in the post pandemic planning of traffic and industry, with the methodology readily adapted for use in other locations where a separation of effects may be beneficial.

Development of point sampling technology for identifying high-emitting vehicles in narrow and deep street canyons

Side road measurements of fine particle (FP) concentrations were performed in two narrow and deep street canyons in the historic center of Naples using point sampling technology (PS). The FP concentration in the range of 20–103 nm was measured using a condensation particle number counter (CPC) with a 1 Hz frequency. Video recording was used to detect passing vehicles classified into three categories: motorcycles, cars, and light-duty vehicles. The two monitored streets were adjacent, with similar geometrics and orientation but different high- and low-traffic levels. FP concentration time series data were processed to identify concentration peaks generated by the exhausts of passing vehicles. A sensitivity analysis study was carried out to determine how data processing parameters could affect high-emitting vehicle (high emitter) identification. Once the optimum parameters were defined, the contribution of high emitters exhaust emissions to local air quality was assessed. The results showed that PS technology offered viable opportunities for application in narrow street canyons where deploying more common crossroad and top-down monitoring can be troublesome. However, some issues remain to be solved to accurately identify high emitterss in high-traffic cases. PS technology can be useful for developing effective air quality management policies in historical centres.

Estimating Indoor Pollutant Loss Using Mass Balances and Unsupervised Clustering to Recognize Decays.

Low-cost air quality monitors are increasingly being deployed in various indoor environments. However, data of high temporal resolution from those sensors are often summarized into a single mean value, with information about pollutant dynamics discarded. Further, low-cost sensors often suffer from limitations such as a lack of absolute accuracy and drift over time. There is a growing interest in utilizing data science and machine learning techniques to overcome those limitations and take full advantage of low-cost sensors. In this study, we developed an unsupervised machine learning model for automatically recognizing decay periods from concentration time series data and estimating pollutant loss rates. The model uses k-means and DBSCAN clustering to extract decays and then mass balance equations to estimate loss rates. Applications on data collected from various environments suggest that the CO2 loss rate was consistently lower than the PM2.5 loss rate in the same environment, while both varied spatially and temporally. Further, detailed protocols were established to select optimal model hyperparameters and filter out results with high uncertainty. Overall, this model provides a novel solution to monitoring pollutant removal rates with potentially wide applications such as evaluating filtration and ventilation and characterizing indoor emission sources.

Effects of freeze-thaw cycles on methanogenic hydrocarbon degradation: Experiment and modeling

Cold regions are warming much faster than the global average, resulting in more frequent and intense freeze-thaw cycles (FTCs) in soils. In hydrocarbon-contaminated soils, FTCs modify the biogeochemical and physical processes controlling petroleum hydrocarbon (PHC) biodegradation and the associated generation of methane (CH4) and carbon dioxide (CO2). Thus, understanding the effects of FTCs on the biodegradation of PHCs is critical for environmental risk assessment and the design of remediation strategies for contaminated soils in cold regions. In this study, we developed a diffusion-reaction model that accounts for the effects of FTCs on toluene biodegradation, including methanogenic biodegradation. The model is verified against data generated in a 215 day-long batch experiment with soil collected from a PHC contaminated site in Ontario, Canada. The fully saturated soil incubations with six different treatments were exposed to successive 4-week FTCs, with temperatures oscillating between −10 °C and +15 °C, under anoxic conditions to stimulate methanogenic biodegradation. We measured the headspace concentrations and 13C isotope compositions of CH4 and CO2 and analyzed the porewater for pH, acetate, dissolved organic and inorganic carbon, and toluene. The numerical model represents solute diffusion, volatilization, sorption, as well as a reaction network of 13 biogeochemical processes. The model successfully simulates the soil porewater and headspace concentration time series data by representing the temperature dependencies of microbial reaction and gas diffusion rates during FTCs. According to the model results, the observed increases in the headspace concentrations of CH4 and CO2 by 87% and 136%, respectively, following toluene addition are explained by toluene fermentation and subsequent methanogenesis reactions. The experiment and the numerical simulation show that methanogenic degradation is the primary toluene attenuation mechanism under the electron acceptor-limited conditions experienced by the soil samples, representing 74% of the attenuation, with sorption contributing to 11%, and evaporation contributing to 15%. Also, the model-predicted contribution of acetate-based methanogenesis to total produced CH4 agrees with that derived from the 13C isotope data. The freezing-induced soil matrix organic carbon release is considered as an important process causing DOC increase following each freezing period according to the calculations of carbon balance and SUVA index. The simulation results of a no FTC scenario indicate that, in the absence of FTCs, CO2 and CH4 generation would decrease by 29% and 26%, respectively, and that toluene would be biodegraded 23% faster than in the FTC scenario. Because our modeling approach represents the dominant processes controlling PHC biodegradation and the associated CH4 and CO2 fluxes, it can be used to analyze the sensitivity of these processes to FTC frequency and duration driven by temperature fluctuations.

Research on Gas Concentration Prediction Based on the ARIMA-LSTM Combination Model

The current single gas prediction model is not sufficient for identifying and processing all the characteristics of mine gas concentration time series data. This paper proposes an ARIMA-LSTM combined forecasting model based on the autoregressive integrated moving average (ARIMA) model and the long short-term memory (LSTM) recurrent neural network. In the ARIMA-LSTM model, the ARIMA model is used to process the historical data of gas time series and obtain the corresponding linear prediction results and residual series. The LSTM model is used in further analysis of the residual series, predicting the nonlinear factors in the residual series. The prediction results of the combined model are compared separately with those of the two single models. Finally, RMSE, MAPE and R2 are used to evaluate the prediction accuracy of the three models. The results of the study show that the metrics of the combined ARIMA-LSTM model are R2 = 0.9825, MAPE = 0.0124 and RMSE = 0.083. The combined model has the highest prediction accuracy and the lowest error and is more suitable for the predictive analysis of gas data. By comparing the prediction results of a single model and the combined model on gas time series data, the applicability, validity and scientificity of the combined model proposed in this paper are verified, which is of great importance to accurate prediction and early warning of underground gas danger in coal mines.

A Multi-Model Prediction Method for Coal Mine Gas Concentration with Hierarchical Structure

ABSTRACT The low concentration of gas in the gas blending process is influenced by a number of factors and is characterized by some time variation and non-linearity. Therefore, the gas concentration needs to be predicted. This paper proposes a multi-model forecasting method with a hierarchical structure. First, because the measured gas concentration time-series data contain a lot of noise, the time-series data are decomposed into several independent eigenmode functions by using empirical mode decomposition, adaptively denoising by low-pass filtering, and then using phase space reconstruction technology to obtain a new time-series sample. Then, the training samples are grouped by conditional fuzzy clustering to determine the number of sub-modules. Finally, the maximum membership method is used to select sub-models and sub-sub-models, and then a multi-model time-series prediction model is established. The model can not only select different sub-models to process data in different regions but also can process each data jointly by multiple sub-models in different sub-models. Experiments were carried out on low-concentration measured data extracted from mines. The experimental results show that the proposed prediction model can capture the nonlinear characteristics of gas concentration time series and is superior to other existing prediction models in accuracy.

Ensemble Machine Learning Model for Accurate Air Pollution Detection Using Commercial Gas Sensors.

This paper presents the results on developing an ensemble machine learning model to combine commercial gas sensors for accurate concentration detection. Commercial gas sensors have the low-cost advantage and become key components of IoT devices in atmospheric condition monitoring. However, their native coarse resolution and poor selectivity limit their performance. Thus, we adopted recurrent neural network (RNN) models to extract the time-series concentration data characteristics and improve the detection accuracy. Firstly, four types of RNN models, LSTM and GRU, Bi-LSTM, and Bi-GRU, were optimized to define the best-performance single weak models for CO, O3, and NO2 gases, respectively. Next, ensemble models which integrate multiple single weak models with a dynamic model were defined and trained. The testing results show that the ensemble models perform better than the single weak models. Further, a retraining procedure was proposed to make the ensemble model more flexible to adapt to environmental conditions. The significantly improved determination coefficients show that the retraining helps the ensemble models maintain long-term stable sensing performance in an atmospheric environment. The result can serve as an essential reference for the applications of IoT devices with commercial gas sensors in environment condition monitoring.

An Improved Imputation Method for Accurate Prediction of Imputed Dataset Based Radon Time Series

This article primarily focuses on the performance evaluation of a new methodology, imputation by feature importance (IBFI), to serve its imputed dataset in further regression scenarios when dealing with soil radon gas concentration (SRGC) time-series data. The time-series data have been collected spanning over fourteen(14) months period, which included four seismic events, and have been used for experimentation. The imputation by feature importance (IBFI) has been experimented and obtained results are found more efficient in the imputation of missing patterns in investigated time series when compared to traditionally used imputation methods viz. mean, median, mode, predictive mean matching (PMM), and hot-deck imputation.The IBFI methodology has been used in a variety of settings, such as data missing not at random (MNAR), missing completely at random (MCAR), and missing at random (MAR), with missingness percentages ranging from 10% to 30%. In this study, the imputed datasets, 9 for each imputation method, have been used further to predict the attribute of interest (radon concentration (RN)) keeping others as independent attributes such as thoron, temperature, relative humidity, and pressure time series. Support vector machine (SVM) with linear kernel has been used as a learning algorithm and its performance was evaluated based on the fact that how efficient and unbiased values were imputed. Statistical performance evaluation measures viz. root mean squared log error (RMSLE), root mean square error (RMSE), mean squared error (MSE),and mean absolute percentage error (MAPE) have been calculated for the assessment of performance. The findings of our study show that the IBFI imputed dataset has provided a better-fitted model. The model generation and predictions upon IBFI imputed time series result in more accurate predictions when compared to mean, median, mode, PMM, and hot-deck imputed time series. Furthermore, PMM and median imputed time series also perform closer to the IBFI imputed time series.

PM2.5 concentration distribution patterns and influencing meteorological factors in the central and eastern China during 1980–2018

The long-term global record of surface PM2.5 mass concentration is one of the important products from the Modern-Era Retrospective Analysis for Research and Applications (MERRA-2). This long-time record began in the 1980s, whereby MERRA-2 has facilitated the study of the interactions between air pollution and climate change in China. In this study, the principal component analysis (PCA) method was applied to classify the PM2.5 concentration distribution representing the primary states. It was also used to investigate relationships between PM2.5 pollution distribution types and atmospheric circulation based on NASA MERRA-2 surface PM2.5 mass concentration and ERA-5 reanalysis data. The distribution of the PM2.5 concentrations in the central and eastern China over the winter was classified into five types. A typical pollution model (Type 2) was linked to the decrease in sea-land temperature differences as a result of increasing in land temperature, weakened air flow, reduced precipitation, stable vertical temperature, and relative humidity formation. For the typical clean models (Types 3 and 4), the meteorological conditions differed greatly from those of Type 2. Type 3 was related to the thermal effects of the Qinghai-Tibet Plateau, causing the diffusion conditions in the bottom layer in eastern China and being detrimental to pollution accumulation. Type 4 was related to good pollution diffusion conditions, caused by cold air and high pressure. Types 1 and 5 represent marginal pollution that are closely related to precipitation, pollution transport, temperature, and the vertical structure of relative humidity. A Gaussian log-likelihood mutation test of PM2.5 concentration time series data in the central and eastern China showed that the mutation point appeared around the year 2000. This is related to the increasing impacts of climate change, such as rising land temperature and falling sea level pressure. This study provides new insights into the relationship between the distribution types of PM2.5 pollution and atmospheric circulation; it elucidates the potential dynamic mechanisms controlling the severity and distribution of heavy air pollution.

An automated computational approach to kinetic model discrimination and parameter estimation.

We herein report experimental applications of a novel, automated computational approach to chemical reaction network (CRN) identification. This report shows the first chemical applications of an autonomous tool to identify the kinetic model and parameters of a process, when considering both catalytic species and various integer and non-integer orders in the model's rate laws. This kinetic analysis methodology requires only the input of the species within the chemical system (starting materials, intermediates, products, etc.) and corresponding time-series concentration data to determine the kinetic information of the chemistry of interest. This is performed with minimal human interaction and several case studies were performed to show the wide scope and applicability of this process development tool. The approach described herein can be employed using experimental data from any source and the code for this methodology is also provided open-source.

Sediment Bed Borehole Advection Method

This paper introduces and tests the Sediment Bed Borehole Advection Method (SBBAM), a low cost, point-measurement technique which utilizes a push-point probe to quantify the vertical direction and magnitude of Darcy flux at the surface water—groundwater sediment interface. The Darcy flux measurements are derived from the residence-time analysis of tracer arrival calculated from measured tracer concentration time-series data. The technique was evaluated in the laboratory using a sediment bed simulator tank at eight flow rates (1–90 cm/day). Triplicate test runs for each flow rate returned average errors between 4–20 percent; r2 = 0.9977.

Leak localization using distributed sensors and machine learning for hydrogen releases from a fuel cell vehicle in a parking garage

The detection and localization of hydrogen releases are of vital importance in large confined spaces to enable safe commercial penetration of hydrogen energy into society. In the present study, a hydrogen leak localization system was developed based on measured concentration data and a machine learning system. A scale parking garage model was used to experimentally model fuel cell vehicle leaks in a confined space with helium used as a surrogate of hydrogen for safety reasons. Twelve gas sensors were placed along the ceiling to measure the helium concentrations. The leak position was changed in each test to gather a range of concentration data. Then, the measured helium concentrations were used as input data to train the machine learning models. Two machine learning algorithms were used to locate the leak position, an artificial neural network (ANN) and the K-DTW algorithm. The helium concentrations recorded by the twelve sensors at a specific time point were used as the input data for the ANN model, while time-series concentration data was used as the input for the K-DTW algorithm. The results show that the ANN model can predict leak locations that were not included in the training datasets and that the K-DTW algorithm can classify the unknown leak location to the given location label. The ANN prediction accuracy was 78.4%. The K-DTW algorithm identified the leak location with 87.5% accuracy. The localization technology developed in the present work can provide safe monitoring of large parking garages and the model accuracy can be improved by feeding more training data to the machine learning models.

Extracting mass concentration time series features for classification of indoor and outdoor atmospheric particulates

Particulate matters (PMs) are considered as one of the air pollutants generally associated with poor air quality in both outdoor and indoor environments. The composition, distribution and size of these particles hazardously affect the human health causing cardiovascular health problems, lung dysfunction, respiratory problems, chronic obstructive pulmonary disease and lungs cancer. Classification models developed by analyzing mass concentration time series data of atmospheric particulate matter can be used for the prediction of air quality and for issuing warnings to protect the health of the public. In this study, mass concentration time series data of both outdoor and indoor particulates matters PM2.5 (aerodynamics size up to 2.5 μ) and PM10.0 (aerodynamics size up to 10.0 μ) were acquired using Haz-Dust EPAM-5000 from six different locations of the Muzaffarabad city, Azad Kashmir. The linear and nonlinear approaches were used to extract mass concentration time series features of the indoor and outdoor atmospheric particulates. These features were given as an input to the robust machine learning classifiers. The support vector machine (SVM) kernels, ensemble classifiers, decision tree and K-nearest neighbors (KNN) are used to classify the indoor and outdoor particulate matter time series. The performance was estimated in terms of area under the curve (AUC), accuracy, true negative rate, true positive rate, negative predictive value and positive predictive value. The highest accuracy (95.8%) was obtained using cubic and coarse Gaussian SVM along with the cosine and cubic KNN, while the highest AUC, i.e., 1.00, is obtained using fine Gaussian and cubic SVM as well as with the cubic and weighted KNN.

Database of pharmacokinetic time-series data and parameters for 144 environmental chemicals

Time courses of compound concentrations in plasma are used in chemical safety analysis to evaluate the relationship between external administered doses and internal tissue exposures. This type of experimental data is rarely available for the thousands of non-pharmaceutical chemicals to which people may potentially be unknowingly exposed but is necessary to properly assess the risk of such exposures. In vitro assays and in silico models are often used to craft an understanding of a chemical’s pharmacokinetics; however, the certainty of the quantitative application of these estimates for chemical safety evaluations cannot be determined without in vivo data for external validation. To address this need, we present a public database of chemical time-series concentration data from 567 studies in humans or test animals for 144 environmentally-relevant chemicals and their metabolites (187 analytes total). All major administration routes are incorporated, with concentrations measured in blood/plasma, tissues, and excreta. We also include calculated pharmacokinetic parameters for some studies, and a bibliography of additional source documents to support future extraction of time-series. In addition to pharmacokinetic model calibration and validation, these data may be used for analyses of differential chemical distribution across chemicals, species, doses, or routes, and for meta-analyses on pharmacokinetic studies.

Investigation of Entrance and Wall Dynamics of the High-Flux Gas-Solid Riser Using Statistical Analysis of Solids Concentration Signals

Statistical analysis of the entrance and wall dynamics of a high-flux gas-solid riser was done using solid concentration time series data collected from a 76 mm internal diameter and 10 m high riser of a CFB system with a twin-riser operated at 4.0 to 10.0 m/s gas velocity and 50 to 550 kg/m2s solids flux. Spent fluid catalytic cracking catalyst particles with 67 μm mean diameter and density of 1500 kg/m3 together with 70% to 80% humid air was used. Solid concentration data were analysed using code prepared using FORTRAN 2008 to get statistical parameters and plot their profiles. Results obtained show that the gas-solid suspension flow in the riser is dominated by low solid concentration in the centre region and high solid concentration in the wall region which forms a core-annulus flow structure. The mean solid concentration in the wall region decreases with riser height from the dense bottom section to less dense in the fully developed flow section at the top of the riser. The gas-solid suspension flow in the centre region is dominated with uniform flow structure while the wall region is dominated with high fluctuations in solid concentration. Further, it was found that the entrance and developing flow sections of the riser exhibit high flow non-uniformities than the fully developed flow section of the riser. The flow non-uniformities in the entrance and developing flow section increase with increase in superficial gas velocity at constant solid flux. The wall region, from the entrance to the top sections of the riser along the axial direction exhibits both dilute and dense suspension flow.

Phosphorus and nitrogen trajectories in the Mediterranean Sea (1950–2030): Diagnosing basin-wide anthropogenic nutrient enrichment

Human activities have significantly modified the inputs of land-derived phosphorus (P) and nitrogen (N) to the Mediterranean Sea (MS). Here, we reconstruct the external inputs of reactive P and N to the Western Mediterranean Sea (WMS) and Eastern Mediterranean Sea (EMS) over the period 1950–2030. We estimate that during this period the land derived P and N loads increased by factors of 3 and 2 to the WMS and EMS, respectively, with reactive P inputs peaking in the 1980s but reactive N inputs increasing continuously from 1950 to 2030. The temporal variations in reactive P and N inputs are imposed in a coupled P and N mass balance model of the MS to simulate the accompanying changes in water column nutrient distributions and primary production with time. The key question we address is whether these changes are large enough to be distinguishable from variations caused by confounding factors, specifically the relatively large inter-annual variability in thermohaline circulation (THC) of the MS. Our analysis indicates that for the intermediate and deep water masses of the MS the magnitudes of changes in reactive P concentrations due to changes in anthropogenic inputs are relatively small and likely difficult to diagnose because of the noise created by the natural circulation variability. Anthropogenic N enrichment should be more readily detectable in time series concentration data for dissolved organic N (DON) after the 1970s, and for nitrate (NO3) after the 1990s. The DON concentrations in the EMS are predicted to exhibit the largest anthropogenic enrichment signature. Temporal variations in annual primary production over the 1950–2030 period are dominated by variations in deep-water formation rates, followed by changes in riverine P inputs for the WMS and atmospheric P deposition for the EMS. Overall, our analysis indicates that the detection of basin-wide anthropogenic nutrient concentration trends in the MS is rendered difficult due to: (1) the Atlantic Ocean contributing the largest reactive P and N inputs to the MS, hence diluting the anthropogenic nutrient signatures, (2) the anti-estuarine circulation removing at least 45% of the anthropogenic nutrients inputs added to both basins of the MS between 1950 and 2030, and (3) variations in intermediate and deep water formation rates that add high natural noise to the P and N concentration trajectories.