Particulate Matter Time Series Research Articles

Retrieving PM10 Surface Concentration from AERONET Aerosol Optical Depth: The Cairo and Delhi Megacities Case Studies

Large concentrations of air-suspended particulate matter (PM) in megacities represent an important health risk for their populations, but PM time series are often missing or too short to quantify the associated burden of diseases. In this study, we propose a model for retrieving the surface PM in Cairo (Egypt) and Delhi (India) from the automated measurements of aerosol optical depth (AOD), precipitable water (PW), and Angström exponent (AE) performed by the sunphotometers of the Aerosol Robotics Network (AERONET). For this we exploit the (1) synchronous measurements performed from 2010 to 2015 at the headquarters of the Egyptian Meteorological Authority and in 2009 at the Gual Pahari station (25 km south of Delhi) and (2) the ERA5 estimate of the planetary boundary layer height (H). The correlation between the surface PM10 and the AOD is primarily controlled by the variations of PW and secondarily by those of H: for similar surface PM10 concentrations, the AOD tends to be the largest in summer because of the hygroscopic enhancement of the mass extinction efficiency (σ) of the particles and their dilution in the more developed mixing layer. The variations of composition also play a significant role in Cairo. This effect, particularly marked in spring (coinciding with the dust season), can be parameterized as a linear function of AE. Finally, we show that the variations of the surface PM10 concentration at the two sites can be retrieved simply from those of the AOD, PW, AE and H. At the weekly temporal resolution, the agreement between the model and the observations is very good at the two locations (correlation coefficient > 0.81, relative mean absolute error < 15%). This validates indirectly the assumption made in the development of the model, namely that the aerosols are mostly confined to the mixing layer of the two megacities and not transported in the free atmosphere. Provided a few years of surface PM measurements are available, the methodology proposed in this study could be easily applied to any other AERONET station.

Ambient Air Quality and General Health Outcomes in Nigde (Turkey) between 2011 and 2017

Background:We aimed to provide information for health practitioners and other related people about the association between ambient air quality and adverse health outcomes in the general population of Nigde, a central Turkish city, within the context of current health data epidemiological evidence.Methods:The present study highlights the connection between health problems and time series of particulate matter (PM10) and sulphur dioxide (SO2) in Nigde, Turkey between 2011 and 2017. Significant morbidity is linked to ambient air pollution, resulting in a significant economic cost to society.Results:We found that the required funds to treat cancers and chronic obstructive pulmonary disease triggered by ambient air pollution in Nigde, exceed 9 million US dollars per year, even when only the city center is taken into account.Conclusion:As Turkish cities grow and urban population density increases, air pollution issues need to be given priority in order to protect the health of the public and support sustainable development for future generations. It is recommended that particulate matter concentration in this urban center should be significantly reduced to minimize health problems.

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.

동태적 조건부 상관성을 이용한 미세먼지 자료분석

동태적 조건부 상관성을 이용한 미세먼지 자료분석

Forecasting of particulate matter time series using wavelet analysis and wavelet-ARMA/ARIMA model in Taiyuan, China

ABSTRACTParticulate matter with aerodynamic diameter below 10 μm (PM10) forecasting is difficult because of the uncertainties in describing the emission and meteorological fields. This paper proposed a wavelet-ARMA/ARIMA model to forecast the short-term series of the PM10 concentrations. It was evaluated by experiments using a 10-year data set of daily PM10 concentrations from 4 stations located in Taiyuan, China. The results indicated the following: (1) PM10 concentrations of Taiyuan had a decreasing trend during 2005 to 2012 but increased in 2013. PM10 concentrations had an obvious seasonal fluctuation related to coal-fired heating in winter and early spring. (2) Spatial differences among the four stations showed that the PM10 concentrations in industrial and heavily trafficked areas were higher than those in residential and suburb areas. (3) Wavelet analysis revealed that the trend variation and the changes of the PM10 concentration of Taiyuan were complicated. (4) The proposed wavelet-ARIMA model could be efficiently and successfully applied to the PM10 forecasting field. Compared with the traditional ARMA/ARIMA methods, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction.Implications: Wavelet analysis can filter noisy signals and identify the variation trend and the fluctuation of the PM10 time-series data. Wavelet decomposition and reconstruction reduce the nonstationarity of the PM10 time-series data, and thus improve the accuracy of the prediction. This paper proposed a wavelet-ARMA/ARIMA model to forecast the PM10 time series. Compared with the traditional ARMA/ARIMA method, this wavelet-ARMA/ARIMA method could effectively reduce the forecasting error, improve the prediction accuracy, and realize multiple-time-scale prediction. The proposed model could be efficiently and successfully applied to the PM10 forecasting field.

Particulate matter time-series and Köppen-Geiger climate classes in North America and Europe

Four years of time-series data on the particulate matter (PM) concentrations from 801 monitoring stations located in Europe and 234 stations in North America were analyzed. Using k-means clustering with distance correlation as a measure for similarity, 5 distinct PM clusters in Europe and 9 clusters across the United States of America (USA) were found. This study shows that meteorology has an important role in controlling PM concentrations, as comparison between Köppen-Geiger climate zones and identified PM clusters revealed very good spatial overlapping. Moreover, the Köppen-Geiger boundaries in Europe show a high similarity to the boundaries as defined by PM clusters. The western USA is much more diverse regarding climate zones; this characteristic was confirmed by cluster analysis, as 6 clusters were identified in the west, and only 3 were identified on the eastern side of the USA. The lowest similarity between PM time-series in Europe was observed between the Iberian Peninsula and the north Europe clusters. These two regions also show considerable differences, as the cold semi-arid climate has a long and hot summer period, while the cool continental climate has a short summertime and long and cold winters. Additionally, intra-continental examination of European clusters showed meteorologically driven phenomena in autumn 2011 encompassing a large European region from Bulgaria in the south, Germany in central Europe and Finland in the north with high PM concentrations in November and a decline in December 2011. Inter-continental comparison between Europe and the USA clusters revealed a remarkable difference between the PM time-series located in humid continental zone. It seems that because of higher shortwave downwelling radiation (≈210 W m−2) over the USA's continental zone, and consequently more intense production of secondary aerosols, a summer peak in PM concentration was observed. On the other hand, Europe's humid continental climate region experiences lower solar radiation (≈180 W m−2); consequently, the elevated summer-time PM concentrations were not detected.

Temporal Patterns in Fine Particulate Matter Time Series in Beijing: A Calendar View.

Extremely high fine particulate matter (PM2.5) concentration has become synonymous to Beijing, the capital of China, posing critical challenges to its sustainable development and leading to major public health concerns. In order to formulate mitigation measures and policies, knowledge on PM2.5 variation patterns should be obtained. While previous studies are limited either because of availability of data, or because of problematic a priori assumptions that PM2.5 concentration follows subjective seasonal, monthly, or weekly patterns, our study aims to reveal the data on a daily basis through visualization rather than imposing subjective periodic patterns upon the data. To achieve this, we conduct two time-series cluster analyses on full-year PM2.5 data in Beijing in 2014, and provide an innovative calendar visualization of PM2.5 measurements throughout the year. Insights from the analysis on temporal variation of PM2.5 concentration show that there are three diurnal patterns and no weekly patterns; seasonal patterns exist but they do not follow a strict temporal division. These findings advance current understanding on temporal patterns in PM2.5 data and offer a different perspective which can help with policy formulation on PM2.5 mitigation.

Correlating respiratory disease incidences with corresponding trends in ambient particulate matter and relative humidity

Investigation over 14 months was undertaken at a representative rural location in the state of Himachal Pradesh to understand the putative correlation between the reported high Respiratory Disease Incidences (RDI) with air/particulate pollution exposure in a time series based investigations. Time series data on RDI cases from public health centers of Jawali, the sampling location, was obtained along with the corresponding time series data of ambient particulate matter (PM) concentrations in two size fractions (PM10 and PM2.5). The time series of PM associated carbon forms — elemental carbon (EC), black carbon (BC), organic carbon (OC), and UV absorbing organic compounds (UVOC)— and meteorological factors were taken into consideration as explanatory variables. De-composition of respective time series data-sets using Empirical Ensemble Mode De-composition of separating trends from the multiple cyclic influences of variable periods enabled to establish a correlation in the RDI trends with trends in ambient PM2.5 concentrations and Relative Humidity (RH). Multiple linear regression analysis adequately explained 99% of the variation in the RDI trends as a function of the trends in ambient PM2.5 and relative humidity (RH); 77% of the variation was explained by the trends in PM2.5 and 22% by RH.

A wavelet-based approach applied to suspended particulate matter time series in Portugal

This study intends to analyse the particulate matter (PM) levels in Portugal (mainland and islands) throughout a 3-year period. Although a decreasing trend has been observed, the WHO guidelines for the PM10 and PM2.5 annual mean concentrations have been exceeded in all monitoring stations. Most inland urban, rural and suburban sites follow a pronounced seasonal variation with much higher values in winter than in summer. Lower levels and a weak seasonal variability were registered in the two urban background stations of Madeira Island, which are permanently under the influence of clean air masses over the Atlantic. Receiving long-range transported pollution, rural stations located in mountain sites presented an opposite seasonal pattern, with higher levels in summer. Diurnal profiles were also analysed and compared between stations. A mining process was also carried out, consisting in the application of multi-scale wavelet transforms, data pattern identification using cluster analysis and examination of the contribution to the total variance/covariance of the time series per wavelet scale for all stations. Groups of stations exhibiting similar variance/covariance profiles were identified. One group contains urban and rural stations with diurnal and daily time scales. Urban background stations located in the island of Madeira constitute another cluster, corresponding to higher wavelet scales (lower periodicity phenomena). One traffic station in the Oporto metropolitan area was grouped with a suburban/industrial station of central Portugal, suggesting the need for reclassification in what concerns the type of environmental influence.

Functional Clustering of Particulate Matter less than 10 Microns: A Case of Africa

Particulate matter (PM) air pollution is a challenge that is endangering the environment and human health in Africa. Most countries in Africa are poor, hence monitoring of PM-related aspects is a challenge. Clustering of aspects of PM can ease the burden. In this paper, time series (TS) of PM of less than 10 microns (PM10) for 51 African countries are clustered. The data are represented in functional form and complete-linkage hierarchical clustering algorithm is used to cluster the coefficients of the functional data, thereby clustering the original TS. The functional form has the advantage of looking at the trajectories as a whole. 2 clusters are extracted from the data. 2014 Cross-sectional data of PM of less than 2.5 microns (PM2.5) for African countries are also clustered and 3 clusters are obtained. Adjusted Rand Index (ARI) computed for clusters resulting from the two data sets is 0.588, indicating some agreement on the clusters resulting from the two data sets.

An Approach to Improve the Performance of PM Forecasters.

The particulate matter (PM) concentration has been one of the most relevant environmental concerns in recent decades due to its prejudicial effects on living beings and the earth’s atmosphere. High PM concentration affects the human health in several ways leading to short and long term diseases. Thus, forecasting systems have been developed to support decisions of the organizations and governments to alert the population. Forecasting systems based on Artificial Neural Networks (ANNs) have been highlighted in the literature due to their performances. In general, three ANN-based approaches have been found for this task: ANN trained via learning algorithms, hybrid systems that combine search algorithms with ANNs, and hybrid systems that combine ANN with other forecasters. Independent of the approach, it is common to suppose that the residuals (error series), obtained from the difference between actual series and forecasting, have a white noise behavior. However, it is possible that this assumption is infringed due to: misspecification of the forecasting model, complexity of the time series or temporal patterns of the phenomenon not captured by the forecaster. This paper proposes an approach to improve the performance of PM forecasters from residuals modeling. The approach analyzes the remaining residuals recursively in search of temporal patterns. At each iteration, if there are temporal patterns in the residuals, the approach generates the forecasting of the residuals in order to improve the forecasting of the PM time series. The proposed approach can be used with either only one forecaster or by combining two or more forecasting models. In this study, the approach is used to improve the performance of a hybrid system (HS) composed by genetic algorithm (GA) and ANN from residuals modeling performed by two methods, namely, ANN and own hybrid system. Experiments were performed for PM2.5 and PM10 concentration series in Kallio and Vallila stations in Helsinki and evaluated from six metrics. Experimental results show that the proposed approach improves the accuracy of the forecasting method in terms of fitness function for all cases, when compared with the method without correction. The correction via HS obtained a superior performance, reaching the best results in terms of fitness function and in five out of six metrics. These results also were found when a sensitivity analysis was performed varying the proportions of the sets of training, validation and test. The proposed approach reached consistent results when compared with the forecasting method without correction, showing that it can be an interesting tool for correction of PM forecasters.

Prediction of particulate matters at urban intersection by using multilayer perceptron model based on principal components

The time series of particulate matter at urban intersection consists of complex linear and non-linear patterns and are difficult to predict. Multilayer perceptron (MLP) model has been applied to air quality prediction in urban areas, but it has limited accuracy owing to the co-linearity between the input variables. To overcome it, a novel hybrid model combining MLP model and principal component analysis (PCA) is proposed to improve the prediction accuracy. The PCA was applied before the MLP model was implemented to generate principal components as input variables, rather than using the original data, to reduce the complexity and eliminate data co-linearity. The proposed model is examined with the measured data of particulate matter (PM) concentrations in spring and winter respectively. Experimental results indicated that the hybrid model can be an effective tool to improve the particulate matter prediction accuracy. Additionally, the proposed model is found to perform better for analyzing PM1 levels than PM10. The model is also verified to produce better predictions in winter than that in spring. The outputs of these findings demonstrate the potential of the proposed model to be applied to predict the trends of air pollution in similar meso- to mega- cities.

Prediction of particulate matter at street level using artificial neural networks coupling with chaotic particle swarm optimization algorithm

The time series of particulate matter at urban intersection consists of complex linear and nonlinear patterns and are difficult to forecast. Artificial neural networks (ANNs) have been applied to air quality forecasting in urban areas, but they have limited accuracy owing to their potential convergence to a local minimum and over-fitting. Chaotic particle swarm optimization (CPSO) algorithm is chaos-based searching algorithms and can recognize nonlinear patterns. Hence, a novel hybrid model combining ANN and CPSO algorithm is proposed to improve forecast accuracy. The proposed model, together with the ANN model with the traditional algorithms (Levenberg–Marquardt and PSO), is examined with the measured data in spring and winter respectively. The proposed model is found to provide the best results among them, implying that the hybrid model can be an effective tool to improve the particulate matter forecasting accuracy. Additionally, the proposed model is found to perform better for fine particles than for coarse particles. The model is also verified to predict better in winter than in spring. The outputs of these findings demonstrate the potential of the proposed model to be applied to forecast the trends of air pollution in similar meso-to mega-cities.

Outdoor 222Radon concentrations monitoring in relation with particulate matter levels and possible health effects

Air quality monitoring could potentially improve exposure estimates for use in epidemiological studies. We investigated air quality by monitoring concentrations of 222Rn near the ground and particulate matter (PM) with an aerodynamic diameter less than or equal to 10 μm (PM10) and 2.5 μm (PM2.5) for Bucharest-Magurele periurban area. Atmospheric radon concentrations have been continuously monitored near the ground at 1 m height as well as at 10 m height. This paper presents time-series of radon concentrations monitoring in air near the ground measured during 1 January 2011–1 January 2012 by use of solid state nuclear track detectors SSNTD CR-39, exposed for 10 days periods. The daily average atmospheric radon concentration near the ground registered at 1 m height was found to be in range of 40.25 ± 7.53 Bq/m3, which was comparable with the daily average radon concentration of 44.92 ± 9.94 Bq/m3 recorded for period 1 August 2011–20 December 2011 at 10 m height by AlphaGUARD Radon monitor. Also, was done a comparative analysis of spatio-temporal variations in time series of outdoor radon concentration and PM in two size fractions (PM10 and PM2.5) in Bucharest Magurele area for 2011 year. The predominant recorded component in PM10 was PM2.5. Observational results show that recorded yearly average PM2.5 and PM10 concentrations were 35.96 μg/m3 and 40.91 μg/m3, respectively. The average ratio of PM2.5/PM10 was 87.9 % at this sampling site. However, in densely populated Bucharest urban and suburban areas the mean daily EC limit values for PM10, PM2.5 and attached 222Rn are frequently exceeded leading to serious public concern during the last years. The ambient air pollution measurements like as PM10 and PM2.5 levels are used as a proxy for personal exposure levels. Have been investigated also meteorological effects on the temporal patterns of atmospheric radon and particle matter.

Modeling the transport of Saharan dust toward the Mediterranean region: an important issue for its ecological implications

Airborne particulate matter (PM) is presently an environmental problem of primary concern, whose role in air quality, climatic and ecological issues is well recognized, though still a matter of extensive investigations (Intergovernmental Panel on Climate Change, 2007). It is of great scientific interest to detect sources of atmospheric particulate matter and quantify their influence on the global and local scales. Unfortunately, emissions are usually not directly available, while PM concentration time series are experimentally accessible, so that the problem often consists in inverting these data to determine the region of influence that caused the measured concentration. In this paper we are concerned with an alternative approach to inverse modeling based on backward trajectory analysis (BTA); this approach has the potential to overcome some limitations associated with traditional BTA. We apply this method to the analysis of PM time series from the Monte Cimone observatory, hereafter MCT, a high altitude station on the top of the Italian Northern Apennines, with the aim of estimate the contribution of Saharan dust transport on PM concentration levels registered in the Mediterranean region.

Modeling the transport of Saharan dust toward the Mediterranean region: an important issue for its ecological implications

Airborne particulate matter (PM) is presently an environmental problem of primary concern, whose role in air quality, climatic and ecological issues is well recognized, though still a matter of extensive investigations (Intergovernmental Panel on Climate Change, 2007).It is of great scientific interest to detect sources of atmospheric particulate matter and quantify their influence on the global and local scales. Unfortunately, emissions are usually not directly available, while PM concentration time series are experimentally accessible, so that the problem often consists in "inverting" these data to determine the region of influence that caused the measured concentration. In this paper we are concerned with an alternative approach to inverse modeling based on backward trajectory analysis (BTA); this approach has the potential to overcome some limitations associated with traditional BTA.We apply this method to the analysis of PM time series from the Monte Cimone observatory, hereafter MCT, a high altitude station on the top of the Italian Northern Apennines, with the aim of estimate the contribution of Saharan dust transport on PM concentration levels registered in the Mediterranean region.

INVESTIGATING THE TEMPORAL FLUCTUATIONS IN PARTICULATE MATTER DATA

The temporal fluctuations of particulate matter time series of three reference European stations have been investigated, by using the power spectrum analysis. Our results point out to the presence in particulate matter of annual periodicities superimposed on a scaling behaviour with exponent ranging between ~1.4 and ~1.6, indicating quite high persistent correlations. Furthermore, a crossover timescale at about 1 month, evidenced in all the signals analysed, could be linked with chemical-physical processes in which aerosol particles are involved during their atmospheric lifetimes.

Climate‐based sensitivity of air quality to climate change scenarios for the southwestern United States

AbstractThe need to understand future trends in air quality is an issue that is frequently raised by air quality planners and managers. The potential for extreme events is of particular interest, but forecasts are difficult using traditional methods and further complicated by predictions of future climate change. This study aims to address this research need through a climate‐based sensitivity study of ground‐level ozone and particulate matter (PM) in the U.S. Southwest. Extreme value methods were used to examine ozone and PM in Tucson, Arizona, over the time period 1990–2001. A Statistical DownScaling Model (SDSM) was employed to build regression‐based models between gridded meteorological data and point time series of ozone and PM. Following model calibration and verification, future climate‐based ozone and PM scenarios were created for 2002–2050 and 2051–2099. Daily output of projected ozone and PM were then subjected to extreme value methods in order to estimate climate‐based changes in ozone and PM extremes over the next century. Results indicate that monthly mean and extreme ozone values are sensitive to predicted increases in temperature, particularly in the summer months. PM results are less certain but suggest that PM may be sensitive to changing moisture conditions. Copyright © 2008 Royal Meteorological Society

A new approach for combining information available from multiple particulate air pollution monitors

In time-series studies on the effect of particulate matter (PM) air pollution on an adverse health outcome, PM time-series data are often available from multiple monitoring stations. Published studies have combined the data from the multiple monitors using a simple or trimmed average. We investigate an alternative method of combining the data available from multiple PM-monitoring sites. This method uses time-series data to assign each PM monitor a weight. The weights are then used to combine the data from the multiple PM monitors into a single air pollution time series. The resulting model will identify important monitors for describing the relationship between PM and the adverse health outcome of interest. Subsequent investigations of why certain monitors are more informative than others may provide valuable information concerning the location of vulnerable subpopulations or locations where the meteorological and/or land-use conditions are better for assessing population exposure to PM. The new model is illustrated by applying it to actual data from Cook County, IL, USA and through a simulation study. Using the new model, for the Cook County data, it was found that two of the six monitors provided essentially as much information about the effect of PM on mortality as all six monitors combined. The simulation study suggests that the weights assigned to each monitor by the new model are appropriate, that is, that the model assigns the largest weight to the monitor most highly correlated with the underlying PM time series used to generate mortality.

Referent selection in case-crossover analyses of acute health effects of air pollution.

The case-crossover design was proposed for the study of a transient effect of an intermittent exposure on the subsequent occurrence of a rare acute-onset disease. This design can be an alternative to Poisson time series regression for studying the health effects of fine particulate matter air pollution. Characteristics of time-series of particulate matter, including long-term time trends, seasonal trends, and short-term autocorrelations, require that referent selection in the case-crossover design be considered carefully and adapted to minimize bias. We performed simulations to evaluate the bias associated with various referent selection strategies for a proposed case-crossover study of associations between particulate matter and primary cardiac arrest. Some a priori reasonable strategies were associated with a relative bias as large as 10%, but for most strategies the relative bias was less than 2% with confidence interval coverage within 1% of the nominal level. We show that referent selection for case-crossover designs raises the same issues as selection of smoothing method for time series analyses. In addition, conditional logistic regression analysis is not strictly valid for some case-crossover designs, introducing further bias.