Measured Air Quality Data Research Articles

Ensemble-based classification approach for PM2.5 concentration forecasting using meteorological data.

Air pollution is a serious challenge to humankind as it poses many health threats. It can be measured using the air quality index (AQI). Air pollution is the result of contamination of both outdoor and indoor environments. The AQI is being monitored by various institutions globally. The measured air quality data are kept mostly for public use. Using the previously calculated AQI values, the future values of AQI can be predicted, or the class/category value of the numeric value can be obtained. This forecast can be performed with more accuracy using supervised machine learning methods. In this study, multiple machine-learning approaches were used to classify PM2.5 values. The values for the pollutant PM2.5 were classified into different groups using machine learning algorithms such as logistic regression, support vector machines, random forest, extreme gradient boosting, and their grid search equivalents, along with the deep learning method multilayer perceptron. After performing multiclass classification using these algorithms, the parameters accuracy and per-class accuracy were used to compare the methods. As the dataset used was imbalanced, a SMOTE-based approach for balancing the dataset was used. Compared to all other classifiers that use the original dataset, the accuracy of the random forest multiclass classifier with SMOTE-based dataset balancing was found to provide better accuracy.

Comparison Process of Blood Heavy Metals Absorption Linked to Measured Air Quality Data in Areas with High and Low Environmental Impact

Air pollution is a problem shared by the entire world population, and researchers have highlighted its adverse effects on human health in recent years. The object of this paper was the relationship between the pollutants’ concentrations measured in the air and the quantity of pollutant itself inhaled by the human body. The area chosen for the study has a high environmental impact given the significant presence on the territory of polluting activities. The Acerra area (HI) has a waste-to-energy plant and numerous industries to which polluting emissions are attributed. This area has always been the subject of study as the numbers of cancer patients are high. A survey on male patients to evaluate the heavy metals concentrations in the blood was conducted in the two areas and then linked to its values aero-dispersed. Using the air quality data measured by the monitoring networks in two zones, one with high environmental impact (HI) and one with low environmental impact (LI), the chronicle daily intake (CDI) of pollutants inhaled by a single person was calculated. The pollutants considered in this study are PM10 and four heavy metals (As, Cd, Ni, Pb) constituting the typical particulates of the areas concerned. The CDI values calculated for the two zones are significantly higher in the HI zone following the seasonal pollution trend.

Assessment of global and regional PM10 CAMSRA data: comparison to observed data in Morocco.

Given the strong impact of air quality on health, environment, and economy, Morocco has implemented an air quality network to assess air pollutants including PM10 (particulate matter with a diameter less than 10 μm). This network which is composed of 29 fixed measurement stations is spatially limited and does not provide sufficient time resolution. The scarcity of measured air quality data led to seek an optimal alternative source to conduct related data-based studies. This represents the primary objective of this paper. PM10 concentrations of global Copernicus Atmosphere Monitoring Service Reanalysis (CAMSRA) data (4D Variational analysis "4v" and analysis "an"), as well as regional CAMSRA data, were examined against the average daily PM10 concentrations collected from six fixed Moroccan air quality measurement stations in 2016 (i.e., observation data). The verification is carried out by studying and analyzing seasonal, extreme, and annual values. The study shows a strong seasonal dependence with a positive bias in winter and a negative bias during summer. For the study of extreme values, global CAMSRA "an" and "4v" data record significant bias of approximately 184 and 161 μg/m3, respectively. However, the annual analysis shows that the CAMSRA global "an" data have the smallest average bias (20.008 μg/m3) and hence has the closest representation of observation data. We conclude that the CAMSRA global analysis data could be used to compute climatology, study trends, evaluate models, benchmark other reanalysis, or serve as boundary conditions for regional models for past periods.

Modeling the Effect of COVID-19 Lockdown on Mobility and NO2 Concentration in the Lombardy Region

Recent observation and modeling-based studies have shown how air quality has been positively affected by the containment measures enforced due to the COVID-19 outbreak. This work aims to analyze Lombardy’s NO2 atmospheric concentration during the spring lockdown. The region of Lombardy is known for having the largest number of residents in Italy and high levels of pollution. It is also the region where the first European confinement measures were imposed by the Italian government. The modeling suite composed of CAMx (Comprehensive Air Quality Model with Extensions) and WRF (Weather Research and Forecasting model) provides the setting to compare the atmospheric NO2 concentration from mid-February to the end of March with a business as usual situation. The main interest in this work is to investigate the response of NO2 atmospheric concentration to increasingly reduced road traffic. We can simulate, for the first time, a real circumstance of progressively reduced mobility, as well as validating it with measured air quality data. Focusing on the city of Milan, we found that the decrease in NO2 concentration reflects progressively reduced traffic contraction. In the case of a large traffic abatement (71%), the concentration level is reduced by one third. We also find that industrial activities have a relevant impact on NO2 atmospheric concentration, especially in the provinces of Brescia and Bergamo. This study provides an overview of how incisive policies must be implemented to achieve the set environmental targets and protect human health.

Enhanced data validation strategy of air quality monitoring network

Quick validation and detection of faults in measured air quality data is a crucial step towards achieving the objectives of air quality networks. Therefore, the objectives of this paper are threefold: (i) to develop a modeling technique that can be used to predict the normal behavior of air quality variables and help provide accurate reference for monitoring purposes; (ii) to develop fault detection method that can effectively and quickly detect any anomalies in measured air quality data. For this purpose, a new fault detection method that is based on the combination of generalized likelihood ratio test (GLRT) and exponentially weighted moving average (EWMA) will be developed. GLRT is a well-known statistical fault detection method that relies on maximizing the detection probability for a given false alarm rate. In this paper, we propose to develop GLRT-based EWMA fault detection method that will be able to detect the changes in the values of certain air quality variables; (iii) to develop fault isolation and identification method that allows defining the fault source(s) in order to properly apply appropriate corrective actions. In this paper, reconstruction approach that is based on Midpoint-Radii Principal Component Analysis (MRPCA) model will be developed to handle the types of data and models associated with air quality monitoring networks. All air quality modeling, fault detection, fault isolation and reconstruction methods developed in this paper will be validated using real air quality data (such as particulate matter, ozone, nitrogen and carbon oxides measurement).

Air quality over Portugal in 2020

This works intends to evaluate the impacts of the national emission ceilings (NEC) reduction scenarios on the air quality in Portugal, verifying the fulfillment of the air quality thresholds for 2020. The air quality numerical modeling system WRF–EURAD was applied to this 2020 future scenario and results were compared to the present situation -year 2012. This modeling system was already evaluated for Portugal domain in previous studies, by comparison with measured air quality data, and showed reasonable skills for all the pollutants. This system was applied over the Continental domain of Portugal, using nesting approach, with a horizontal resolution of 5x5km2, for both scenarios conditions (2012 and 2020) considering the respective emissions data and assuming the 2012 meteorological conditions. The results point towards an improvement of the air quality over Continental Portugal, in particular for particulate matter (in the urban areas of Lisbon and Porto) and SO2 (near specific industrial sources) but do not solve the non–compliance status regarding the O3 threshold value for protection of human health. These results strengthen the importance of including the NEC emission scenarios in the air quality national strategy, but additional mitigation actions need to be designed, with focus on ozone and its precursors, at local and regional scale.

CALPUFF and AERMOD Model Validation Study in the Near Field: Martins Creek Revisited

ABSTRACT This paper describes a near-field validation study involving the steady-state, U.S. Environmental Protection Agency (EPA) guideline model AERMOD and the nonsteady-state puff model CALPUFF. Relative model performance is compared with field measurements collected near Martins Creek, PA—a rural, hilly area along the Pennsylvania-New Jersey border. The principal emission sources in the study were two coal-fired power plants with tall stacks and buoyant plumes. Over 1 yr of sulfur dioxide measurements were collected at eight monitors located at or above the two power plants' stack tops. Concurrent meteorological data were available at two sites. Both sites collected data 10 m above the ground. One of the sites also collected sonic detection and ranging measurements up to 420 m above ground. The ability of the two models to predict monitored sulfur dioxide concentrations was assessed in a four-part model validation. Each part of the validation applied different criteria and statistics to provide a comprehensive evaluation of model performance. Because of their importance in regulatory applications, an emphasis was placed on statistics that demonstrate the model's ability to reproduce the upper end of the concentration distribution. On the basis of the combined results of the four-part validation (i.e., weight of evidence), the performance of CALPUFF was judged to be superior to that of AERMOD. IMPLICATIONS Use of the nonsteady-state CALPUFF model in the near field (<50 km) for regulatory applications has been limited because of the lack of appropriate model validation studies. Considered an alternative model by EPA, use of CALPUFF for regulatory purposes in the near field must be supported by a relevant performance evaluation using measured air quality data. This validation study should help address the lack of information on the performance of CALPUFF in near-field applications. The potential problem with the use of the robust high concentration as a metric in model validations is also examined.

A New Cost Function for Air Quality Modeling

In this paper a new Back-Propagation (BP) algorithm cost function is appropriately studied for the modeling of air pollution time series. The underlying idea is that of modifying the error definition in order to improve the capabilities of this kind of models to forecast episodes of poor air quality. The proposed error definition can be regarded as a generalization of the traditional squared error cost function thanks to the presence of a parameter ? which allows to obtain the ordinary BP as a special case when ??=?1. A criterion for choosing this parameter is stated based on setting a-priori a maximum level of allowable false alarms. The goodness of the proposed approach is assessed by means of case studies both on synthetic and measured air quality data.

INTEGRATED MODELING OF ROAD TRAFFIC EMISSIONS: APPLICATION TO LISBON AIR QUALITY MANAGEMENT

The application of three distinct modeling tools to deal with Lisbon atmospheric problems is presented. Information and forecasting system for private and public transport (VISUM), Transport Emission Model for Line Sources (TREM) and Variable Dispersion (VADIS) models were used to characterize the traffic fluxes, to quantify the emission amounts, and finally to evaluate the air quality in a specific area of the city characterized by intense traffic respectively. The results show the benefit of integrated use of the modeling tools VISUM and TREM to estimate the atmospheric emissions induced by traffic. On the other hand, the results obtained with VADIS are in acceptable agreement with the measured air quality data.

Ambient air quality for the territory of the Czech Republic in 1996–1999 expressed by three essential factors

A new method for spatial interpretation and visualisation of measured air quality data developed and introduced in 1998 is used for ambient air quality assessment in the Czech Republic. The data from a countrywide air quality monitoring network is collated into the least possible number of factors describing the overall air quality in 1996–1999. The factors identified as ‘ambient air pollution’, ‘ground-level ozone’ and ‘wet atmospheric deposition’ represent three different aspects of ambient air quality and their impact on receptors’ evaluation. The monitoring stations are classified for each factor using five categories clearly distinguishing the sites over the Czech Republic territory within the ‘best–worst’ scale range. The results are presented in maps with spot symbols produced in Arc/View system. The problem areas within the country are identified clearly for each factor. The air quality picture differs considerably for the respective factors within one calendar year. Categorisation of each factor for the entire period under review, however, remains strikingly similar, indicating that in spite of partial improvement and some decreases in air pollutant concentration, the problem areas have remained more or less the same for each factor. Comparison of all three factors shows that the southern part of the country is in a certain simplification very clean as to the ‘ambient air pollution’ and ‘wet atmospheric deposition’ factors, while it is impacted as to the ‘ground-level ozone’ factor. In contrast, the northern part of the country impacted as to the ‘ambient air pollution’ along with the ‘wet atmospheric deposition’ and ‘ground-level ozone’ factors at the higher elevations.

Spatial interpretation of ambient air quality for the territory of the Czech Republic

A method for spatial interpretation and visualisation of measured air quality data is presented that may serve as a tool for ambient air quality characterisation using the least possible number of factors. This method enables comparison of different areas in relative terms and has practical consequences in decision making and public information. The 1996 data for the Czech Republic was used as a database. According to my results, not a single universal indicator can fully describe the ambient air quality albeit the three factors identified as “ambient air pollution”, “ground-level ozone” and “wet atmospheric deposition” which are recommended. These selected factors represent three different aspects of ambient air quality and its impact on receptors. For the above factors black and white charts are presented classifying the Czech Republic territory into five categories as to relative ambient air quality. The air quality picture differs for the respective factors considerably.

Application of a parallel photochemical air quality model to the Campania region (southern Italy)

During most of the year, the concentrations of both primary and secondary air pollutants over the Campania region (southern Italy) do not comply with the Italian air quality standards. To gain insight into the chemical and meteorological processes that lead to high air pollutant concentrations over this area, the parallel package PNAM (Parallel Naples Airshed Model) has been developed, for the numerical simulation of photosmog episodes on urban and regional scale domains. PNAM has been applied to a photosmog episode which occurred on 26 July 1995. On this day, due to the stagnant conditions and the intensive solar radiation, a high ozone concentration was reported for the Naples basin. The performance of PNAM has been assessed by comparing measured air quality data with simulated data for O 3, NO, NO 2 and CO. PNAM was able to reproduce temporal and spatial characteristics of measured air quality data, although some discrepancies were evident, probably mainly due to the emission inventory, which was based only on total annual emissions.

Passenger aircraft cabin air quality: trends, effects, societal costs, proposals

As aircraft operators have sought to substantially reduce propulsion fuel cost by flying at higher altitudes, the energy cost of providing adequate outside air for ventilation has increased. This has lead to a significant decrease in the amount of outside air provided to the passenger cabin, partly compensated for by recirculation of filtered cabin air. The purpose of this review paper is to assemble the available measured air quality data and some calculated estimates of the air quality for aircraft passenger cabins to highlight the trend of the last 25 years. The influence of filter efficiencies on air quality, and a few medically documented and anecdotal cases of illness transmission aboard aircraft are discussed. Cost information has been collected from the perspective of both the airlines and passengers. Suggestions for air quality improvement are given which should help to result in a net, multistakeholder savings and improved passenger comfort.

Numerical Optimization Techniques in Air Quality Modeling: Objective Interpolation Formulas for the Spatial Distribution of Pollutant Concentration

Abstract A technique is proposed for objection interpolation of the air quality distribution over a region in terms of sparse measurement data. Empirical information provided by the latter is effectively combined with knowledge of atmospheric dispersion functions of the type commonly used in source-oriented air quality models, to provide improved estimates of the concentration distribution over an extended region. However, the technique is not primarily source-oriented since, in contrast to the real source distribution of a source-oriented model, it utilizes fictitious or pseudo sources that are estimated in terms of the measured air quality data. This involves the use of interpolation functions that are computed using numerical optimization techniques based on the method of least squares. Due to the large number of different “weather” states that affect the atmospheric dispersion of pollution, considerable computation is required, although the bulk of this can be done in advance, so that the final interp...