Investigation on the statistical distribution of PM2.5 concentration in Chiang Mai, Thailand

Recently, it is found that Northern Thailand has very high levels of airborne particulates known as PM2.5. PM2.5 particulates can cause breathing problems and may raise the risks of heart disease and even some cancers. According to AirVisual, Chiang Mai, the capital of Northern Thailand which offers for tourists in both business and cultural center, had the highest levels of smog in the world in March 2018, reaching at least 183 on the PM2.5 Air Quality Index scale. The daily average PM2.5 concentration data are determined from July 2016 – June 2018 at two stations in Chiang Mai at Yupparaj Wittayalai school and City Hall. The Weibull, Gamma, Lognormal and Inverse Gaussian distributions are considered for finding the most appropriate probability functions of the daily average PM2.5 concentration. The results show that, as evaluated with the goodness- of-fit measures; Komolgorov-Smirnov and Anderson-Darling test statistics, the Inverse Gaussian distribution is the most suitable probability density functions of the daily average PM2.5 concentration for two stations. Furthermore, the return periods of the PM2.5 concentration are predicted by using the Largest Extreme Value distribution, which can be further applied in air quality management and related policy making.

Download Full-text

Effect of Air Quality on Cardio-Respiratory Systems in Northern Thailand (Chiang Mai, Chiang Rai and Nan Province)

Chiang Mai University Journal of Natural Sciences ◽

10.12982/cmujns.2020.0045 ◽

2020 ◽

Vol 19 (4) ◽

Author(s):

Ketwarang Leelasittikul ◽

◽

Patcharee Koonkumchoo ◽

Sasipa Buranapuntalug ◽

Karan Pongpanit ◽

...

Keyword(s):

Air Quality ◽

Chiang Mai ◽

Northern Thailand ◽

Respiratory Systems

Download Full-text

The Statistical Boundary Polygon of a Two Parameter Stochastic Process

Volume 1: Offshore Technology; Offshore Wind Energy; Ocean Research Technology; LNG Specialty Symposium ◽

10.1115/omae2006-92179 ◽

2006 ◽

Author(s):

Torfinn Ottesen ◽

Jon A. Aarstein

Keyword(s):

Convex Hull ◽

Goodness Of Fit ◽

Structural Integrity ◽

Time Series Data ◽

Extreme Values ◽

Extreme Value Distribution ◽

Series Data ◽

Goodness Of Fit Test ◽

Anderson Darling ◽

Two Parameter

The simultaneous values of tension and bending are of primary interest when checking the structural integrity of dynamic risers and umbilicals. Considering extreme checks, it is generally sufficient to check the simultaneous values along the convex hull of the extreme contour. A method for obtaining a boundary polygon that approximates the statistical extreme convex hull from time series data is described. The procedure for obtaining the extreme values from the most suitable extreme value distribution as determined by the Anderson-Darling Goodness-of-Fit test is outlined.

Download Full-text

Air Quality and Environmental Injustice in India: Connecting Particulate Pollution to Social Disadvantages

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18010304 ◽

2021 ◽

Vol 18 (1) ◽

pp. 304

Author(s):

Jayajit Chakraborty ◽

Pratyusha Basu

Keyword(s):

Air Pollution ◽

Air Quality ◽

Transport Model ◽

Spatial Clustering ◽

Fine Particulate Matter ◽

Central India ◽

Chemical Transport Model ◽

Concentration Data ◽

Northern India ◽

Pm2.5 Concentration

While air pollution levels in India are amongst the highest in the world, the link between exposure to air pollution and social disadvantages has not been systematically examined. Using a distributive environmental justice framework, this study connects fine particulate matter (PM2.5) concentration data derived from satellite observations, a global chemical transport model, and ground-based measurements to district level socio-demographic information from the 2011 Census of India. The research objectives are to determine if annual average PM2.5 concentrations (2010) and recent increases in average PM2.5 concentrations (2010–2016) are unequally distributed with respect to socially disadvantaged population and household groups, after controlling for relevant contextual factors and spatial clustering. Overall, more than 85% of people and households in India reside in districts where international air quality standards for PM2.5 are exceeded. Although PM2.5 concentration levels are significantly higher in more urbanized districts located predominantly in northern India, recent increases have occurred in less urbanized areas located mainly in southern and central India. Multivariable statistical analysis indicated: (1) higher PM2.5 concentration in districts with higher percentages of Scheduled Castes (SCs), young children, and households in poor condition residence and without toilets; and (2) higher PM2.5 increases in less urbanized districts with higher percentages of SCs, females, children, people with disabilities, and households with no toilets. These findings thus highlight the need to consider the role of air pollution in exacerbating the consequences of social disadvantages in India.

Download Full-text

Filling the gaps of in situ hourly PM<sub>2.5</sub> concentration data with the aid of empirical orthogonal function analysis constrained by diurnal cycles

Atmospheric Measurement Techniques ◽

10.5194/amt-13-1213-2020 ◽

2020 ◽

Vol 13 (3) ◽

pp. 1213-1226 ◽

Cited By ~ 4

Author(s):

Kaixu Bai ◽

Ke Li ◽

Jianping Guo ◽

Yuanjian Yang ◽

Ni-Bin Chang

Keyword(s):

Air Quality ◽

Empirical Orthogonal Function ◽

Diurnal Cycle ◽

Ambient Air ◽

Concentration Data ◽

Diurnal Variability ◽

Orthogonal Function ◽

Good Prediction Accuracy ◽

Data Gaps ◽

Pm2.5 Concentration

Abstract. Data gaps in surface air quality measurements significantly impair the data quality and the exploration of these valuable data sources. In this study, a novel yet practical method called diurnal-cycle-constrained empirical orthogonal function (DCCEOF) was developed to fill in data gaps present in data records with evident temporal variability. The hourly PM2.5 concentration data retrieved from the national ambient air quality monitoring network in China were used as a demonstration. The DCCEOF method aims to reconstruct the diurnal cycle of PM2.5 concentration from its discrete neighborhood field in space and time firstly and then predict the missing values by calibrating the reconstructed diurnal cycle to the level of valid PM2.5 concentrations observed at adjacent times. The statistical results indicate a high frequency of data gaps in our retrieved hourly PM2.5 concentration record, with PM2.5 concentration measured on about 40 % of the days suffering from data gaps. Further sensitivity analysis results reveal that data gaps in the hourly PM2.5 concentration record may introduce significant bias to its daily averages, especially during clean episodes at which PM2.5 daily averages are observed to be subject to larger uncertainties compared to the polluted days (even in the presence of the same amount of missingness). The cross-validation results indicate that our suggested DCCEOF method has a good prediction accuracy, particularly in predicting daily peaks and/or minima that cannot be restored by conventional interpolation approaches, thus confirming the effectiveness of the consideration of the local diurnal variation pattern in gap filling. By applying the DCCEOF method to the hourly PM2.5 concentration record measured in China from 2014 to 2019, the data completeness ratio was substantially improved while the frequency of days with gapped PM2.5 records reduced from 42.6 % to 5.7 %. In general, our DCCEOF method provides a practical yet effective approach to handle data gaps in time series of geophysical parameters with significant diurnal variability, and this method is also transferable to other data sets with similar barriers because of its self-consistent capability.

Download Full-text

PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism

Algorithms ◽

10.3390/a14070208 ◽

2021 ◽

Vol 14 (7) ◽

pp. 208

Author(s):

Jinsong Zhang ◽

Yongtao Peng ◽

Bo Ren ◽

Taoying Li

Keyword(s):

Neural Network ◽

Air Quality ◽

Dropout Rate ◽

Attention Mechanism ◽

Quality Data ◽

Short Term ◽

Concentration Data ◽

Test Set ◽

Term Prediction ◽

Pm2.5 Concentration

The concentration of PM2.5 is an important index to measure the degree of air pollution. When it exceeds the standard value, it is considered to cause pollution and lower the air quality, which is harmful to human health and can cause a variety of diseases, i.e., asthma, chronic bronchitis, etc. Therefore, the prediction of PM2.5 concentration is helpful to reduce its harm. In this paper, a hybrid model called CNN-BiLSTM-Attention is proposed to predict the PM2.5 concentration over the next two days. First, we select the PM2.5 concentration data in hours from January 2013 to February 2017 of Shunyi District, Beijing. The auxiliary data includes air quality data and meteorological data. We use the sliding window method for preprocessing and dividing the corresponding data into a training set, a validation set, and a test set. Second, CNN-BiLSTM-Attention is composed of the convolutional neural network, bidirectional long short-term memory neural network, and attention mechanism. The parameters of this network structure are determined by the minimum error in the training process, including the size of the convolution kernel, activation function, batch size, dropout rate, learning rate, etc. We determine the feature size of the input and output by evaluating the performance of the model, finding out the best output for the next 48 h. Third, in the experimental part, we use the test set to check the performance of the proposed CNN-BiLSTM-Attention on PM2.5 prediction, which is compared by other comparison models, i.e., lasso regression, ridge regression, XGBOOST, SVR, CNN-LSTM, and CNN-BiLSTM. We conduct short-term prediction (48 h) and long-term prediction (72 h, 96 h, 120 h, 144 h), respectively. The results demonstrate that even the predictions of the next 144 h with CNN-BiLSTM-Attention is better than the predictions of the next 48 h with the comparison models in terms of mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2).

Download Full-text

Applying the Anderson-Darling Test to Suicide Clusters

Crisis ◽

10.1027/0227-5910/a000197 ◽

2013 ◽

Vol 34 (6) ◽

pp. 434-437 ◽

Cited By ~ 5

Author(s):

Donald W. MacKenzie

Keyword(s):

Poisson Process ◽

Goodness Of Fit ◽

Small Samples ◽

Massachusetts Institute Of Technology ◽

Homogeneous Poisson Process ◽

Cornell University ◽

The Difference ◽

Suicide Clusters ◽

Institute Of Technology ◽

Anderson Darling

Background: Suicide clusters at Cornell University and the Massachusetts Institute of Technology (MIT) prompted popular and expert speculation of suicide contagion. However, some clustering is to be expected in any random process. Aim: This work tested whether suicide clusters at these two universities differed significantly from those expected under a homogeneous Poisson process, in which suicides occur randomly and independently of one another. Method: Suicide dates were collected for MIT and Cornell for 1990–2012. The Anderson-Darling statistic was used to test the goodness-of-fit of the intervals between suicides to distribution expected under the Poisson process. Results: Suicides at MIT were consistent with the homogeneous Poisson process, while those at Cornell showed clustering inconsistent with such a process (p = .05). Conclusions: The Anderson-Darling test provides a statistically powerful means to identify suicide clustering in small samples. Practitioners can use this method to test for clustering in relevant communities. The difference in clustering behavior between the two institutions suggests that more institutions should be studied to determine the prevalence of suicide clustering in universities and its causes.

Download Full-text

Bootstrap Analysis

10.1093/oso/9780198505044.003.0004 ◽

2017 ◽

Author(s):

Russell Cheng

Keyword(s):

Goodness Of Fit ◽

Null Distribution ◽

Real Data ◽

Confidence Regions ◽

Confidence Bands ◽

Cumulative Distribution ◽

Attractive Alternative ◽

Bootstrap Analysis ◽

Parametric Bootstrapping ◽

Anderson Darling

Parametric bootstrapping (BS) provides an attractive alternative, both theoretically and numerically, to asymptotic theory for estimating sampling distributions. This chapter summarizes its use not only for calculating confidence intervals for estimated parameters and functions of parameters, but also to obtain log-likelihood-based confidence regions from which confidence bands for cumulative distribution and regression functions can be obtained. All such BS calculations are very easy to implement. Details are also given for calculating critical values of EDF statistics used in goodness-of-fit (GoF) tests, such as the Anderson-Darling A2 statistic whose null distribution is otherwise difficult to obtain, as it varies with different null hypotheses. A simple proof is given showing that the parametric BS is probabilistically exact for location-scale models. A formal regression lack-of-fit test employing parametric BS is given that can be used even when the regression data has no replications. Two real data examples are given.

Download Full-text

Forensic and phylogeographic characterization of mtDNA lineages from northern Thailand (Chiang Mai)

International Journal of Legal Medicine ◽

10.1007/s00414-009-0373-4 ◽

2009 ◽

Vol 123 (6) ◽

pp. 495-501 ◽

Cited By ~ 26

Author(s):

Bettina Zimmermann ◽

Martin Bodner ◽

Sylvain Amory ◽

Liane Fendt ◽

Alexander Röck ◽

...

Keyword(s):

Chiang Mai ◽

Northern Thailand ◽

Mtdna Lineages

Download Full-text

Evolution of Gaseous and Particulate Pollutants in the Air: What Changed after Five Lockdown Weeks at a Southwest Atlantic European Region (Northwest of Spain) Due to the SARS-CoV-2 Pandemic?

Atmosphere ◽

10.3390/atmos12050562 ◽

2021 ◽

Vol 12 (5) ◽

pp. 562

Author(s):

Jorge Moreda-Piñeiro ◽

Joel Sánchez-Piñero ◽

María Fernández-Amado ◽

Paula Costa-Tomé ◽

Nuria Gallego-Fernández ◽

...

Keyword(s):

Air Quality ◽

Road Traffic ◽

Statistical Significance ◽

Temporal Trends ◽

Air Pollutant ◽

Concentration Data ◽

Southwest Atlantic ◽

Pollutant Concentrations ◽

Air Quality Monitoring Stations ◽

Almost All

Due to the exponential growth of the SARS-CoV-2 pandemic in Spain (2020), the Spanish Government adopted lockdown measures as mitigating strategies to reduce the spread of the pandemic from 14 March. In this paper, we report the results of the change in air quality at two Atlantic Coastal European cities (Northwest Spain) during five lockdown weeks. The temporal evolution of gaseous (nitrogen oxides, comprising NOx, NO, and NO2; sulfur dioxide, SO2; carbon monoxide, CO; and ozone, O3) and particulate matter (PM10; PM2.5; and equivalent black carbon, eBC) pollutants were recorded before (7 February to 13 March 2020) and during the first five lockdown weeks (14 March to 20 April 2020) at seven air quality monitoring stations (urban background, traffic, and industrial) in the cities of A Coruña and Vigo. The influences of the backward trajectories and meteorological parameters on air pollutant concentrations were considered during the studied period. The temporal trends indicate that the concentrations of almost all species steadily decreased during the lockdown period with statistical significance, with respect to the pre-lockdown period. In this context, great reductions were observed for pollutants related mainly to fossil fuel combustion, road traffic, and shipping emissions (−38 to −78% for NO, −22 to −69% for NO2, −26 to −75% for NOx, −3 to −77% for SO2, −21% for CO, −25 to −49% for PM10, −10 to −38% for PM2.5, and −29 to −51% for eBC). Conversely, O3 concentrations increased from +5 to +16%. Finally, pollutant concentration data for 14 March to 20 April of 2020 were compared with those of the previous two years. The results show that the overall air pollutants levels were higher during 2018–2019 than during the lockdown period.

Download Full-text

The Synergistic Effect of PM2.5 and CO2 Concentrations on Occupant Satisfaction and Work Productivity in a Meeting Room

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18084109 ◽

2021 ◽

Vol 18 (8) ◽

pp. 4109

Author(s):

Jindong Wu ◽

Jiantao Weng ◽

Bing Xia ◽

Yujie Zhao ◽

Qiuji Song

Keyword(s):

Air Quality ◽

Synergistic Effect ◽

Work Productivity ◽

Co2 Concentration ◽

Human Beings ◽

Meeting Room ◽

Negative Effect ◽

Carbon Dioxide Co2 ◽

Occupant Satisfaction ◽

Pm2.5 Concentration

High indoor air quality is crucial for the health of human beings. The purpose of this work is to analyze the synergistic effect of particulate matter 2.5 (PM2.5) and carbon dioxide (CO2) concentration on occupant satisfaction and work productivity. This study carried out a real-scale experiments in a meeting room with exposures of up to one hour. Indoor environment parameters, including air temperature, relative humidity, illuminance, and noise level, were controlled at a reasonable level. Twenty-nine young participants were participated in the experiments. Four mental tasks were conducted to quantitatively evaluate the work productivity of occupants and a questionnaire was used to access participants’ satisfaction. The Spearman correlation analysis and two-way analysis of variance were applied. It was found that the overall performance declined by 1% for every 10 μg/m3 increase in PM2.5 concentration. Moreover, for every 10% increase in dissatisfaction with air quality, productivity performance decreased by 1.1% or more. It should be noted that a high CO2 concentration (800 ppm) has a stronger negative effect on occupant satisfaction towards air quality than PM2.5 concentration in a non-ventilated room. In order to obtain optimal occupant satisfaction and work productivity, low concentrations of PM2.5 (<50 μg/m3) and CO2 (<700 ppm) are recommended.

Download Full-text