Future Pollution Levels Research Articles

Advancing air quality forecasting in Abu Dhabi, UAE using time series models

This research enhances air quality predictions in Abu Dhabi by employing Autoregressive Integrated Moving Average (ARIMA) models on comprehensive air quality data collected from 2015 to 2023. We collected hourly data on nitrogen dioxide (NO2), particulate matter (PM10), and fine particulate matter (PM2.5) from 19 well-placed ground monitoring stations. Our approach utilized ARIMA models to forecast future pollutant levels, with extensive data preparation and exploratory analysis conducted in R. Our results found a significant drop in NO2 levels after 2020 and the highest levels of particulate matter observed in 2022. The findings of our research confirm the effectiveness of the models, indicated by Mean Absolute Percentage Error (MAPE) values ranging from 7.71 to 8.59. Additionally, our study provides valuable spatiotemporal insights into air pollution historical evolution, identifying key times and areas of heightened pollution, which can help in devising focused air quality management strategies. This research demonstrates the potential of ARIMA models in precise air quality forecasting, aiding in proactive public health initiatives and environmental policy development, consistent with Abu Dhabi’s Vision 2030.

Prediction of heavy metal ion distribution and Pb and Zn ion concentrations in the tailing pond area.

The pollution caused by tailings ponds has resulted in ecological damage, with soil contamination significantly impacting the daily lives of residents in the vicinity of mining areas and the future development of mining areas. This study assesses the transport status of heavy metal pollution in tailings areas and predicts its impact on future pollution levels. This study focused on lead-zinc tailing ponds, exploring the spatial and chemical distribution characteristics of heavy metals based on the distributions of Pb, Zn, As, Cu, Cr, Cd, Hg, and Ge ions. The concentrations of the major heavy metal ions Pb and Zn in tailings ponds were predicted via the exponential smoothing method. ① The total accumulation of Pb and Zn in the mine tailings ranges from 936.74~1212.61 mg/kg and 1611.85~2191.47 mg/kg, much greater than the total accumulation of the remaining six heavy metals. The total accumulation of associated heavy metal Cu was high, and the lowest total heavy metals were Hg and Ge at only 0.19 mg/kg and 1.05 mg/kg. ② The analyses of soil heavy metal chemical forms reveal that the heavy metals Pb and Zn had the highest exchangeable state content and state ratio and the strongest transport activity in the industrial plaza and village soils. Pb and Zn are the heavy metals with the greatest eco-environmental impacts in the mining area. ③ The predicted results show that the soil concentrations of the heavy metals Pb and Zn around the tailings area in 2026 are 1.335 and 1.191 times the predicted time starting values. The concentrations of the heavy metals Pb and Zn at the starting point of the forecast are already 3.34 and 3.02 times the upper limits of the environmental standard (according to environmental standards for gravelly grey calcium soils). These results have significant implications for heavy metal pollution risk management.

A Time Series Approach to Smart City Transformation: The Problem of Air Pollution in Brescia

Air pollution is a paramount issue, influenced by a combination of natural and anthropogenic sources, various diffusion modes, and profound repercussions for the environment and human health. Herein, the power of time series data becomes evident, as it proves indispensable for capturing pollutant concentrations over time. These data unveil critical insights, including trends, seasonal and cyclical patterns, and the crucial property of stationarity. Brescia, a town located in Northern Italy, faces the pressing challenge of air pollution. To enhance its status as a smart city and address this concern effectively, statistical methods employed in time series analysis play a pivotal role. This article is dedicated to examining how ARIMA and LSTM models can empower Brescia as a smart city by fitting and forecasting specific pollution forms. These models have established themselves as effective tools for predicting future pollution levels. Notably, the intricate nature of the phenomena becomes apparent through the high variability of particulate matter. Even during extraordinary events like the COVID-19 lockdown, where substantial reductions in emissions were observed, the analysis revealed that this reduction did not proportionally decrease PM2.5 and PM10 concentrations. This underscores the complex nature of the issue and the need for advanced data-driven solutions to make Brescia a truly smart city.

Multi-Step Ahead Ex-Ante Forecasting of Air Pollutants Using Machine Learning

In this study, a novel general multi-step ahead strategy is developed for forecasting time series of air pollutants. The values of the predictors at future moments are gathered from official weather forecast sites as independent ex-ante data. They are updated with new forecasted values every day. Each new sample is used to build- a separate single model that simultaneously predicts future pollution levels. The sought forecasts were estimated by averaging the actual predictions of the single models. The strategy was applied to three pollutants—PM10, SO2, and NO2—in the city of Pernik, Bulgaria. Random forest (RF) and arcing (Arc-x4) machine learning algorithms were applied to the modeling. Although there are many highly changing day-to-day predictors, the proposed averaging strategy shows a promising alternative to single models. In most cases, the root mean squared errors (RMSE) of the averaging models (aRF and aAR) for the last 10 horizons are lower than those of the single models. In particular, for PM10, the aRF’s RMSE is 13.1 vs. 13.8 micrograms per cubic meter for the single model; for the NO2 model, the aRF exhibits 21.5 vs. 23.8; for SO2, the aAR has 17.3 vs. 17.4; for NO2, the aAR’s RMSE is 22.7 vs. 27.5, respectively. Fractional bias is within the same limits of (−0.65, 0.7) for all constructed models.

Air Pollution Monitoring System with Prediction Abilities Based on Smart Autonomous Sensors Equipped with ANNs with Novel Training Scheme

The paper presents a concept of an air pollution monitoring system with prediction abilities, based on wireless smart sensors, that takes into account local conditions (microclimate) prevailing in particular areas of the city. In most cases reported in the literature, artificial neural networks (ANNs) are used to predict future pollution levels. In existing solutions of this type, ANNs are trained with generalized datasets common for larger areas, e.g., cities. Our investigations show, however, that conditions may strongly differ even between particular streets in the city, which may impact prediction quality. This results from varying density of urban development, different levels of insolation, airiness, amounts of greenery, etc. As a result, with similar values of ANN input signals, such as current pollution levels, temperature, pressure, etc., the results of the prediction may differ significantly from reality. For this reason, we propose an innovative solution, in which particular sensors are equipped with miniaturized low-power ANNs, trained with datasets gathered directly from their closest environment, without a need for the obtaining of such data from a base station. This may simplify the installation and maintenance process of a network of such sensors. In a further part of this work, we dealt with solutions that enable the reduction of the computational complexity of ANNs in the case of their implementation on specialized integrated circuits. We propose replacing the most complex mathematical operations used in the learning algorithm with simpler solutions. A prototype chip containing the main blocks of such an ANN was also designed.

The impact of subsidies in a transboundary pollution game with myopic players

In this paper, we study the impact of green subsidies in an international transboundary pollution game. In the game, the developed country only suffers from paying the subsidy but does not reduce emissions, while developing countries reduce their emissions after receive the subsidy. Furthermore, we assume that developing countries can be myopic, which means that they choose their reduction policy without taking into account the future levels of pollution. The results show that when some of the developing countries are myopic, the optimal reduction policy is much lower than that in the case where all of the developing countries are farsighted. The level of pollution is much higher for the former case than the latter one. However, for developed countries, the presence of a certain number of myopic developing countries can maximize their payoffs. Finally, we find the critical condition for a farsighted developing country to adopt a myopic framework. The findings of this paper could offer valuable guidances for climate policy makers.

Pollution State Modelling for Mexico City

SummaryGround level ozone and particulate matter pollutants are associated with a variety of health issues and increased mortality. For this reason, Mexican environmental agencies regulate pollutant levels. In addition, Mexico City defines pollution emergencies by using thresholds that rely on regional maxima for ozone and for particulate matter with diameter less than 10 μm, PM10. To predict local pollution emergencies and to assess compliance to Mexican ambient air quality standards, we analyse hourly ozone and PM10-measurements from 24 stations across Mexico City from 2017 by using a bivariate spatiotemporal model. With this model, we predict future pollutant levels by using current weather conditions and recent pollutant concentrations. Employing hourly pollutant projections, we predict regional maxima needed to estimate the probability of future pollution emergencies. We discuss how predicted compliance to legislated pollution limits varies across regions within Mexico City in 2017. We find that the predicted probability of pollution emergencies is limited to a few time periods. In contrast, we show that predicted exceedance of Mexican ambient air quality standards is a common, nearly daily occurrence.

Comparing CMAQ Forecasts with a Neural Network Forecast Model for PM2.5 in New York

Human health is strongly affected by the concentration of fine particulate matter (PM2.5). The need to forecast unhealthy conditions has driven the development of Chemical Transport Models such as Community Multi-Scale Air Quality (CMAQ). These models attempt to simulate the complex dynamics of chemical transport by combined meteorology, emission inventories (EI’s), and gas/particle chemistry and dynamics. Ultimately, the goal is to establish useful forecasts that could provide vulnerable members of the population with warnings. In the simplest utilization, any forecast should focus on next day pollution levels, and should be provided by the end of the business day (5 p.m. local). This paper explores the potential of different approaches in providing these forecasts. First, we assess the potential of CMAQ forecasts at the single grid cell level (12 km), and show that significant variability not encountered in the field measurements occurs. This observation motivates the exploration of other data driven approaches, in particular, a neural network (NN) approach. This approach makes use of meteorology and PM2.5 observations as model predictors. We find that this approach generally results in a more accurate prediction of future pollution levels at the 12 km spatial resolution scale of CMAQ. Furthermore, we find that the NN is able to adjust to the sharp transitions encountered in pollution transported events, such as smoke plumes from forest fires, more accurately than CMAQ.

The impact of foresight in a transboundary pollution game

We study the impact of foresight in a transboundary pollution game; i.e. the ability of a country to control its emissions taking into account the relationship between current emissions and future levels of pollution and thus on future damages. We show that when all countries are myopic, i.e., choose the ‘laisser-faire’ policy, their payoffs are smaller than when all countries are farsighted, i.e., non-myopic. However, in the case where one myopic country becomes farsighted we show that the welfare impact of foresight on that country is ambiguous. Foresight may be welfare reducing for the country that acquires it. This is due to the reaction of the other farsighted countries to that country’s acquisition of foresight. The country that acquires foresight reduces its emissions while the other farsighted countries extend their emissions. The overall impact on total emissions is ambiguous. Moreover, our results suggest that incentive mechanisms, that involve a very small (possibly zero) present value of transfers, can play an important role in inducing a country to adopt a farsighted behavior and diminishing the number of myopic countries. These incentives would compensate the myopic country for the short-run losses incurred from the acquisition of foresight and can be reimbursed by that country from the gains from foresight that it enjoys in the long run.

Dynamic evaluation of CMAQ part II: Evaluation of relative response factor metrics for ozone attainment demonstrations

The U.S. Environmental Protection Agency provides guidelines on the use of air quality models for projecting whether an emission reduction strategy will lead to future pollutant levels that are at or below the National Ambient Air Quality Standards (NAAQS). The EPA's guidance document for ozone attainment demonstrations recommends an attainment test for the 8-h ozone NAAQS based on using the ratio of output from “future” and “base” model simulations through the calculation of location-specific Relative Response Factors (RRF). The 2007 guidance document as well as other related studies have recommended the use of retrospective evaluation studies in order to evaluate the ability of an air quality model to represent a change in air quality (dynamic evaluation) rather than relying solely on operational evaluation of model predictions under base line conditions. Here simulations from the Community Multiscale Air Quality (CMAQ) modeling system were conducted for 2002 and 2005, a time period characterized by significant emissions reductions associated with the EPA's Nitrogen Oxides State Implementation Plan Call (NOx SIP Call) as well as mobile sources. These simulations were used to evaluate the performance of different forms of the RRF metric for projecting 2002 to 2005 against 2005 observed ozone levels. The evaluation study showed that the current form of the RRF calculation is generally well designed for predicting the future 8-hr ozone “design value” metric used for determining attainment. Specifically, the methodology of using air quality model simulations in a relative sense provided better estimates of future ozone design values than using the modeled future year simulation alone. Alternative forms of the RRF metric were found to be very similar to the current methodology in terms of evaluation metrics. However, alternative RRF metrics were sensitive to the number of days used in the calculation of the RRF. Approaches which used more days in the RRF calculation (relative to the 2007 guidance approach) had slightly higher bias and error in predicting 2005 design values compared to approaches using only a subset of high ozone days.

The Relationship between Particulate Pollution Levels in Australian Cities, Meteorology, and Landscape Fire Activity Detected from MODIS Hotspots

Smoke from bushfires is an emerging issue for fire managers because of increasing evidence for its public health effects. Development of forecasting models to predict future pollution levels based on the relationship between bushfire activity and current pollution levels would be a useful management tool. As a first step, we use daily thermal anomalies detected by the MODIS Active Fire Product (referred to as “hotspots”), pollution concentrations, and meteorological data for the years 2002 to 2008, to examine the statistical relationship between fire activity in the landscapes and pollution levels around Perth and Sydney, two large Australian cities. Resultant models were statistically significant, but differed in their goodness of fit and the distance at which the strength of the relationship was strongest. For Sydney, a univariate model for hotspot activity within 100 km explained 24% of variation in pollution levels, and the best model including atmospheric variables explained 56% of variation. For Perth, the best radius was 400 km, explaining only 7% of variation, while the model including atmospheric variables explained 31% of the variation. Pollution was higher when the atmosphere was more stable and in the presence of on-shore winds, whereas there was no effect of wind blowing from the fires toward the pollution monitors. Our analysis shows there is a good prospect for developing region-specific forecasting tools combining hotspot fire activity with meteorological data.

Learning equilibria in growth‐pollution models

Purpose – This paper seeks to explain how inefficient learning rules may lead to a perception of economic and ecological realities that may be systematically distorted in the long run.Design/methodology/approach – The paper evaluates long‐term growth in standard growth‐pollution models. Expectations about future levels of pollution are formed under adaptive learning.Findings – Socio‐economic players (private agents, governments, non‐profit organizations and/or groups of states) may fail in understanding, with full accuracy, long‐term environmental conditions. The perception about environment threats acquires a cyclical nature, even when ecological problems evolve steadily.Research limitations/implications – Relevant policy implications emerge if the agent is unable to compute the true levels of environmental pollution that will persist in the steady state. Authorities of several kinds are likely to underestimate or overestimate ecological problems.Practical implications – The learning approach to the perc...

Likelihood of Achieving Air Quality Targets under Model Uncertainties

Regulatory attainment demonstrations in the United States typically apply a bright-line test to predict whether a control strategy is sufficient to attain an air quality standard. Photochemical models are the best tools available to project future pollutant levels and are a critical part of regulatory attainment demonstrations. However, because photochemical models are uncertain and future meteorology is unknowable, future pollutant levels cannot be predicted perfectly and attainment cannot be guaranteed. This paper introduces a computationally efficient methodology for estimating the likelihood that an emission control strategy will achieve an air quality objective in light of uncertainties in photochemical model input parameters (e.g., uncertain emission and reaction rates, deposition velocities, and boundary conditions). The method incorporates Monte Carlo simulations of a reduced form model representing pollutant-precursor response under parametric uncertainty to probabilistically predict the improvement in air quality due to emission control. The method is applied to recent 8-h ozone attainment modeling for Atlanta, Georgia, to assess the likelihood that additional controls would achieve fixed (well-defined) or flexible (due to meteorological variability and uncertain emission trends) targets of air pollution reduction. The results show that in certain instances ranking of the predicted effectiveness of control strategies may differ between probabilistic and deterministic analyses.

Impact of future climate changes on high pollution levels

Changes in climate variability as well as changes in extreme weather and climate events in the 20th century, especially those that took place during the last two to three decades of the 20th century, have been discussed in many recent scientific publications. Attempts to project the results of such studies in the future have been made under different assumptions. In this paper, we have chosen one of the well-known scenarios predicting changes of the climate in the world during the last 30 years of the 21st century. This scenario is used, together with several general predictions related to the future climate, to produce three climatic scenarios. The derived climatic scenarios are used to calculate predictions for future pollution levels in Denmark and in Europe by applying the Unified Danish Eulerian Model (UNI-DEM), on a space domain containing the whole of Europe.

Laser Ablation ICP-MS Analysis of Faviidae Corals for Environmental Monitoring of a Tropical Estuary

Environmental Context: The composition of calcium carbonate in annual skeletal growth bands reflects the environmental conditions in which coral grows, enabling their use as long-term bio-monitors and archives of environmental conditions. Such archives will provide a baseline against which recent and future pollutant levels can be compared. Abstract: An LA-ICP-MS linescan procedure has been developed for the determination of sub-millimetre scale elemental compositions of Al, Ba, Cu, La, Mg, Mn, Pb, Rb, Sr, U, and Zn in corallite walls of Faviidae corals, with the aim of establishing coral-based pollution monitoring in a tropical estuary. By selectively analyzing corallite walls, analytical signals from voids, organic, and detrital phases were minimized. Although the relative ablation yields of coral aragonite and NIST glass calibration standards differed and was dependent on pulse energy, accurate internal-standard normalized results could be verified for Ba, Cu, La, Mn, Pb, Sr, U, and Zn through the use of a secondary carbonate standard (MACS-1) and comparison with solution ICP-MS analysis. Signal smoothing and pre-acquisition surface cleaning procedures were applied. Corallite wall compositions varied by factors of up to five over distances of a few hundred micrometres. Significantly, the compositional variations along and perpendicular to the coral growth axis were of similar magnitude on a sub-millimetre scale. Consequently, compositional variations along the growth axis could not be interpreted chronologically on a sub-annual time scale. However, compositional records based on multi-annual integrated line scans should still reflect long-term environmental influences.

Impact on local air quality of the planned fixed link across Øresund

The planned combined bridge and tunnel link between Sweden and Denmark (the Øresund Link) is expected to be in operation around the turn of the century. So far the impacts of the Øresund Link on air pollution have been discussed mainly in terms of changes in emissions, taking into account implications of the link on traffic in the region. It appears that the influence on regional and global air pollution will be marginal. Concerning local effects however, the situation is different; the new link across the Øresund and the island Amager will result in significant changes in traffic pattern and traffic intensity and thus also in air quality in some areas. An investigation initiated in 1994 on the Danish side of the link comprises monitoring of present air pollution and model evaluations of future pollution levels in a series of streets for various traffic scenarios. So far no violations of present limit values have been indicated, but guidelines may be exceeded.

Awareness of Bad News, Environmental Attitudes, and Subjective Estimates of Coastal Pollution

Questionnaires distributed to 154 holiday‐makers on beaches in Southwest England assessed awareness of local hazards or incidents associated with either the electricity supply industry or the water and sewage industry and examined the relationship between awareness and evaluations of the industry, current and future levels of pollution on the beach in question, and general levels of concern about environmental pollution. With respect to electricity, those respondents who were more aware of reports claiming a higher incidence of childhood leukemia in the vicinity of a nearby nuclear plant evaluated the electricity industry as less competent or trustworthy, showed higher levels of environmental concern, and were more pessimistic in their estimates of present and future levels of specific pollutants on their beach. With respect to the water industry, similar effects were associated with greater awareness of an accident at a water treatment plant and agricultural pollution of a nearby estuary. These findings are interpreted as suggesting a cyclical relationship between risk awareness and concern. On the one hand, reports about environmental hazards may lead to generalized concern across specific contexts; on the other hand, greater levels of concern may sensitize individuals to such reports.

Multivariate Stochastic Models of Sulphur Dioxide Pollution in an Urban Area

Three multivariate stochastic mathematical models of daily SO2 pollution in an urban area (Milan, Italy) during the heating season (mid-October/end of March) are illustrated in the paper. Each model is characterized by a different number of external inputs. Precisely, the first model has no inputs (it is simply an autoregressive relationship), the second one has a temperature input (roughly accounting for emission), the third one has two inputs (temperature and wind speed). From each model a real-time predictor is derived, namely a recursive relationship which, at the end of each day, allows future pollution levels to be forecast on the basis of current concentration and meteorological measurements. The quality of the forecast is rather satisfactory, even in episode situations. The improvements in forecast performance when turning from a predictor with less external inputs to a predictor with more external inputs (i.e., when exploiting more information about meteorology) are also pointed out in the paper.

Anti-Pollution Behavior

Earlier studies relating locus of control to social action have produced mixed results. The present investigation examined the moderating effects of perceived outcome on the relationship between activism in the form of anti-pollution behavior and I-E. A representative sample (n = 433) of Winnipeg's adult population was interviewed to obtain data. As predicted, among people optimistic about future levels of pollution, internally oriented individuals engaged in more anti-pollution activities. Among pessimistic respondents there was virtually no relationship between l-E and social actions. Internals also had more accurate information about environmental pollution. representational painting, the evaluation of an abstract painting, the solution rate for anagrams, and elapsed time estimation did not vary as a function of room condition and personality. Sex differences vere also not found to be critical.