A Complex Analysis Employing ARIMA Model and Statistical Methods on Air Pollutants Recorded in Ploiesti, Romania

Air pollution is an everyday issue, very relevant to public authorities, requiring control and monitoring to provide data for decision-making policies. The objective of this study was to evaluate the air quality in Ploiesti city, Romania and to observe the advantages and limitations of the some statistical methods used in forecasting air quality. Data for six air quality parameters collected at monitoring stations in Ploiesti during the 2013 year were statistically analyzed. Principal component analysis (PCA) was used to provide a relevant description in factors that can be explained in terms of different sources of air pollution. The measured pollutants data were statistically analyzed using the auto-regressive integrated moving average (ARIMA) method in order to assess the efficiency of using this method in forecasting the environmental air quality. The results revealed that ARIMA method has some limitations and do not produce satisfactory results for certain air pollutants such as PM10 and CO, even the forecasted period is short. By comparison, the ARIMA model obtained for NOx , NO2 , or O3 time series, provides good results, with relative errors around 5%.

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Spatiotemporal distribution of major pollutants and their health impacts in Hubei Province from 2015 to 2018 based on machine learning to improve LUR

10.5194/egusphere-egu2020-3822 ◽

2020 ◽

Author(s):

Xiao Feng

Keyword(s):

Machine Learning ◽

Air Pollution ◽

Air Quality ◽

Human Health ◽

Air Pollutants ◽

Pollution Prevention ◽

Hubei Province ◽

Spatiotemporal Distribution ◽

Quality Data ◽

Concentration Data

Air pollution poses a serious threat to human health. A large number of studies have shown that certain diseases are closely related to air pollution. Understanding the spatiotemporal distribution of air pollutants and their health effects are of great significance for pollution prevention. This study takes Hubei Province, China as an example. It integrates measured ground air quality data, natural environment data, and socioeconomic data, and uses machine learning to improve the land use regression model to simulate the spatial distribution of concentration PM2.5 / O3 from 2015 to 2018 in the study area. The combined pollutant concentration data and population raster data were used to assess the deaths from specific diseases (stroke, ischemic heart disease, lung cancer) caused by air pollutants. The result shows that high concentrations of pollutants are concentrated in the more economically developed eastern regions of Hubei Province, and the economically backward western regions have good air quality. In addition, the distribution of deaths caused by exposure to air pollution is similar to that of pollutants, which is higher in eastern part of Hubei province. However, the total number of deaths in the province is decreasing year by year. This result shows that environmental governance policies have alleviated the threat of air pollution to human health to some extent. It shows that Hubei Province should combine actual conditions and spatial-temporal distribution characteristics of pollutants to make appropriate environmental protection measures.

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Source Apportionment of Air Pollution: A Case Study In Malaysia

Jurnal Teknologi ◽

10.11113/jt.v72.2934 ◽

2014 ◽

Vol 72 (1) ◽

Cited By ~ 2

Author(s):

Azman Azid ◽

Hafizan Juahir ◽

Mohd Ekhwan Toriman ◽

Azizah Endut ◽

Mohd Khairul Amri Kamarudin ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Source Apportionment ◽

Principal Component ◽

Quality Parameters ◽

Effective Management ◽

Chemometric Technique ◽

Pca Method ◽

Point Pollution

Air pollution is becoming a major environmental issue in Malaysia. This study focused on the identification of potential sources of variations in air quality around the study area based on the data obtained from the Malaysian Department of Environment (DOE). Eight air quality parameters in ten monitoring stations for seven years (2006 – 2012) were gathered. The Principal Component Analysis (PCA) method from chemometric technique was applied to identify the source identification of pollution around the study area. The PCA method has identified methane (CH4), non-methane hydrocarbon (NmHC), total hydrocarbon (THC), ozone (O3) and particulate matter under 10 microns (PM10) are the most significant parameters around the study area. From the study, it can be concluded that the application of the PCA method in chemometric techniques can be applied for the source apportionment purpose. Hence, this study indicated that for the future and effective management of the Malaysian air quality, an effort should be placed as a priority in controlling point and non-point pollution sources.

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Impacts of the COVID-19 lockdown on air quality and its association with human mortality trends in megapolis Mexico City

10.21203/rs.3.rs-90079/v1 ◽

2020 ◽

Author(s):

Gurusamy Kutralam-Muniasamy ◽

Fermín Pérez-Guevara ◽

Priyadarsi D. Roy ◽

I. Elizalde-Martínez ◽

V.C. Shruti

Keyword(s):

Air Pollution ◽

Latin America ◽

Air Quality ◽

Mexico City ◽

Air Pollutants ◽

Mortality Rates ◽

Quality Data ◽

Mortality Trends ◽

Human Mortality ◽

Air Quality Data

Abstract Mexico City is the second most populated city in Latin America, and it went through two partial lockdowns between April 1 and May 31, 2020 for reducing the COVID-19 propagation. The present study assessed air quality and its association with human mortality rates during the lockdown by estimating changes observed in air pollutants (CO, NO2, O3, SO2, PM10 and PM2.5) between the lockdown (April 1 - May 31) and pre-lockdown (January 1 – March 31) periods, as well as by comparing the air quality data of lockdown period with the same interval of previous five-years (2015-2019). Concentrations of NO2 (-29%), SO2 (-55%) and PM10 (-11%) declined and the contents of CO (+1.1%), PM2.5 (+19%) and O3 (+63%) increased during the lockdown compared to the pre-lockdown period. This study also estimated that NO2, SO2, CO, PM10 and PM2.5 reduced by 19-36%, and O3 enhanced by 14% compared to the average of 2015-2019. Reduction in traffic as well as less emission from vehicle exhausts led to remarkable decline in NO2, SO2 and PM10. The significant positive associations of PM2.5, CO and O3 with the numbers of COVID-19 infections and deaths, however, underscored the necessity to enforce air pollution regulations to protect human health in one of the important cities of the northern hemisphere.

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Air Pollution Monitoring Around Residential and Transportation Sector Locations in Lagos Mainland

Journal of Health and Pollution ◽

10.5696/2156-9614-8.19.180903 ◽

2018 ◽

Vol 8 (19) ◽

Cited By ~ 4

Author(s):

Henry E. Obanya ◽

Nnamdi H. Amaeze ◽

Olusola Togunde ◽

Adebayo A. Otitoloju

Keyword(s):

Air Pollution ◽

Particulate Matter ◽

Air Quality ◽

Air Pollutants ◽

Quality Parameters ◽

Transport Sector ◽

Urban Air ◽

Residential Areas ◽

Pm10 And Pm2.5 ◽

Bus Stops

Background. Industrialization and urbanization, while associated with increased productivity, are also potential causes of increased air pollution. Urban air quality has the potential to affect the health and wellbeing of residents of urban areas. Objectives. The present study investigated the levels of air pollutants around residential areas and transport sector locations (TSLs) in Lagos, Nigeria. Residential areas were defined as areas around inner streets and living quarters, while TSLs included busy roads, dual carriage roads, bus stops and major car parks in the Yaba Local Council Development Area of Lagos Mainland, Lagos, Nigeria. Methods. Air quality parameters were assessed in situ using calibrated hand-held devices at selected residential and TSLs. Each sampling location was geo-referenced and concentrations of the various parameters were used to plot distribution maps. Results. The findings from the monitoring exercise showed that levels of the measured air pollutants: carbon monoxide (CO), particulate matter (PM10 and PM2.5), sulphur dioxide (SO2), noise, temperature and humidity were within the ranges of 1.00 – 6.0 5.97 ppm, 43.345.2 – 127.2159.7 μg/m3, 20.3 23.25 – 69.058.16 μg/m3, 0.0 0 – 0.20.17 ppm, 47.7 50 - 65 70.1 dB, 26.2227.2 – 35.536.7°C and 57.0157.6 – 91.8492.3%, respectively, around residential areas. Values of the measured air pollutants at the TSLs ranged as follows: 2.011.0 – 5.397.7 ppm, 103.3360.7 – 179.77404.0 μg/m3, 50.2832.3 – 91.01184.0 μg/m3, 0.00 – 0.40 ppm, 64.2153.1 – 71.1376.3 dB, 27.1826.2 –27.9332.6°C and 60.3660.0 – 75.0178.0%, respectively. Hydrogen sulphide (H2S), ammonia (NH3), nitrogen oxide (NO2) were below detection limits in both sampling locations while volatile organic carbons (VOCs) ranged from 0.00 – 0.10 ppm in the TSLs. Discussion. Most assessed air quality parameters were significantly higher around bus stops (P < 0.05), except for CO and humidity. In addition, PM10 and PM2.5 were much higher than the World Health Organization (WHO) guidelines. The results indicated that the quality of air (particulate matter) in the study area was poor, especially in the TSLs. Conclusions. The Federal Ministry of Environment, through its relevant agencies, must create policies to address urban air pollution, taking into consideration long term exposures and people that are most vulnerable within the population. Competing Interests. The authors declare no competing financial interests.

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Analysis of Air Pollution Trends in Beijing, China

International Journal of Scientific Research in Science and Technology ◽

10.32628/ijsrst207662 ◽

2020 ◽

pp. 372-376

Author(s):

Anbu Clemensis Johnson

Keyword(s):

Air Pollution ◽

Air Quality ◽

Air Pollutants ◽

Ambient Air ◽

Temporal Analysis ◽

Quality Standard ◽

Quality Data ◽

Chinese Government ◽

Average Maximum ◽

Beijing China

Air pollution is a worldwide problem affecting not only the source location, but the globe as a whole. The current study aims to analyse the standard six air pollutants and air quality index (AQI) in Beijing, China. Air quality data was collected from 2014 to 2020 for temporal analysis. The average maximum values of the air pollutants and AQI during the period analysed were, PM2.5: 74.4 µg/m3, PM10: 107.3 µg/m3, SO2: 20.7 µg/m3, CO: 1.5 mg/m3, NO2: 56.3 µg/m3, O3: 173.1 µg/m3 and AQI: 118. Maximum and minimum values of the primary pollutants occurred predominantly during winter and summer months, while O3 exhibited an opposite trend. All air pollutants and AQI declined over the years. Significant reduction of over 50 % was archived for PM2.5, PM10, SO2, CO and less than 5 % for O3. The air pollution trend in Beijing has shown substantial improvement. In 2020, all air pollutants except PM2.5 achieved the national ambient air quality standard. This realisation can be credited to the effective policies implemented by the Chinese government.

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Spatial and temporal variations of air quality and six air pollutants in China during 2015–2017

Scientific Reports ◽

10.1038/s41598-019-50655-6 ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 12

Author(s):

Hong Guo ◽

Xingfa Gu ◽

Guoxia Ma ◽

Shuaiyi Shi ◽

Wannan Wang ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Spatial Resolution ◽

Air Pollutants ◽

Control Policy ◽

Temporal Variations ◽

Spatial And Temporal Variations ◽

Quality Data ◽

Anthropogenic Factors ◽

Air Pollution Control

Abstract Air pollution has aroused significant public concern in China, therefore, long-term air-quality data with high temporal and spatial resolution are needed to understand the variations of air pollution in China. However, the yearly variations with high spatial resolution of air quality and six air pollutants are still unknown for China until now. Therefore, in this paper, we analyze the spatial and temporal variations of air quality and six air pollutants in 366 cities across mainland China during 2015–2017 for the first time to the best of our knowledge. The results indicate that the annual mean mass concentrations of PM2.5, PM10, SO2, and CO all decreased year by year during 2015–2017. However, the annual mean NO2 concentrations were almost unchanged, while the annual mean O3 concentrations increased year by year. Anthropogenic factors were mainly responsible for the variations of air quality. Further analysis suggested that PM2.5 and PM10 were the main factors influencing air quality, while NO2 played an important role in the formation of PM2.5 and O3. These findings can provide a theoretical basis for the formulation of future air-pollution control policy in China.

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Spatial assessment on health impact of atmospheric pollution in Makassar, Indonesia

E3S Web of Conferences ◽

10.1051/e3sconf/202133102019 ◽

2021 ◽

Vol 331 ◽

pp. 02019

Author(s):

Wesam Al Madhoun ◽

Faheem Ahmad Gul ◽

Faizah Che Ros ◽

Hamza Ahmad Isiyaka ◽

Anwar Mallongi ◽

...

Keyword(s):

Principal Component Analysis ◽

Air Pollution ◽

Air Quality ◽

Principal Component ◽

Ambient Air ◽

Component Analysis ◽

Health Impact ◽

Quality Data ◽

Pm10 And Pm2.5 ◽

Starting Point

There has been little discussion to date on air pollution and its potential relationship with health in Makassar, Indonesia. This study aims to create a starting point for this discussion by investigating existing data points and the potential correlation between ambient air pollution and health in Makassar, Indonesia. Six months of air quality data (July-December, 2018) on CO, SO2, NO2, O3, PM10, and PM2.5 were provided by the city and were analyzed alongside tuberculosis and pneumonia data provided by the hospital and community health centers in Makassar. Data were analyzed using principal component analysis, dendrogram, and some GIS mapping. Quantitative data from the USAID-funded Building Health Cities project were also used to help explain some of the quantitative findings. Results show that principal component analysis (PCA) gave three statistics factors having eigenvalues exceeding one, which account for 83% of the total variance in the dataset. The three factors accounted for a strong impact by CO, O3, SO2, PM10, and PM2.5 attributed to the incomplete combustion of fuel from automobiles, bush burning, and industrial emission. Air pollution-related illnesses such as tuberculosis and pneumonia are found to prevail in the area. Real-time air quality monitoring is required to benchmark the health impact of extreme conditions. This study also encourages urgent intervention by decision-makers to tackle the level of tuberculosis and pneumonia occurrence that may be favored by the poor air quality in Makassar.

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Functional ANOVA approaches for detecting changes in air pollution during the COVID-19 pandemic

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-021-02071-4 ◽

2021 ◽

Author(s):

Christian Acal ◽

Ana M. Aguilera ◽

Annalina Sarra ◽

Adelia Evangelista ◽

Tonio Di Battista ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Functional Data ◽

Central Italy ◽

Principal Component ◽

Functional Principal Component Analysis ◽

Area Of Interest ◽

Pm 10 ◽

The Impact ◽

Monitoring Stations

AbstractFaced with novel coronavirus outbreak, the most hard-hit countries adopted a lockdown strategy to contrast the spread of virus. Many studies have already documented that the COVID-19 control actions have resulted in improved air quality locally and around the world. Following these lines of research, we focus on air quality changes in the urban territory of Chieti-Pescara (Central Italy), identified as an area of criticality in terms of air pollution. Concentrations of $$\hbox {NO}_{{2}}$$ NO 2 , $$\hbox {PM}_{{10}}$$ PM 10 , $$\hbox {PM}_{2.5}$$ PM 2.5 and benzene are used to evaluate air pollution changes in this Region. Data were measured by several monitoring stations over two specific periods: from 1st February to 10 th March 2020 (before lockdown period) and from 11st March 2020 to 18 th April 2020 (during lockdown period). The impact of lockdown on air quality is assessed through functional data analysis. Our work makes an important contribution to the analysis of variance for functional data (FANOVA). Specifically, a novel approach based on multivariate functional principal component analysis is introduced to tackle the multivariate FANOVA problem for independent measures, which is reduced to test multivariate homogeneity on the vectors of the most explicative principal components scores. Results of the present study suggest that the level of each pollutant changed during the confinement. Additionally, the differences in the mean functions of all pollutants according to the location and type of monitoring stations (background vs traffic), are ascribable to the $$\hbox {PM}_{{10}}$$ PM 10 and benzene concentrations for pre-lockdown and during-lockdown tenure, respectively. FANOVA has proven to be beneficial to monitoring the evolution of air quality in both periods of time. This can help environmental protection agencies in drawing a more holistic picture of air quality status in the area of interest.

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Scalable kernel-based SVM classification algorithm on imbalance air quality data for proficient healthcare

Complex & Intelligent Systems ◽

10.1007/s40747-021-00435-5 ◽

2021 ◽

Author(s):

Shwet Ketu ◽

Pramod Kumar Mishra

Keyword(s):

Air Pollution ◽

Air Quality ◽

Class Imbalance ◽

Imbalanced Data ◽

Classification Algorithm ◽

Quality Data ◽

Pollution Level ◽

Classification Problems ◽

Chi Square ◽

The Impact

AbstractIn the last decade, we have seen drastic changes in the air pollution level, which has become a critical environmental issue. It should be handled carefully towards making the solutions for proficient healthcare. Reducing the impact of air pollution on human health is possible only if the data is correctly classified. In numerous classification problems, we are facing the class imbalance issue. Learning from imbalanced data is always a challenging task for researchers, and from time to time, possible solutions have been developed by researchers. In this paper, we are focused on dealing with the imbalanced class distribution in a way that the classification algorithm will not compromise its performance. The proposed algorithm is based on the concept of the adjusting kernel scaling (AKS) method to deal with the multi-class imbalanced dataset. The kernel function's selection has been evaluated with the help of weighting criteria and the chi-square test. All the experimental evaluation has been performed on sensor-based Indian Central Pollution Control Board (CPCB) dataset. The proposed algorithm with the highest accuracy of 99.66% wins the race among all the classification algorithms i.e. Adaboost (59.72%), Multi-Layer Perceptron (95.71%), GaussianNB (80.87%), and SVM (96.92). The results of the proposed algorithm are also better than the existing literature methods. It is also clear from these results that our proposed algorithm is efficient for dealing with class imbalance problems along with enhanced performance. Thus, accurate classification of air quality through our proposed algorithm will be useful for improving the existing preventive policies and will also help in enhancing the capabilities of effective emergency response in the worst pollution situation.

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Contribution of Local and Transboundary Air Pollution to the Urban Air Quality of Fukuoka, Japan

Atmosphere ◽

10.3390/atmos12040431 ◽

2021 ◽

Vol 12 (4) ◽

pp. 431

Author(s):

Ayako Yoshino ◽

Akinori Takami ◽

Keiichiro Hara ◽

Chiharu Nishita-Hara ◽

Masahiko Hayashi ◽

...

Keyword(s):

Air Pollution ◽

Air Quality ◽

Urban Areas ◽

Air Pollutants ◽

Regional Scale ◽

Gaseous Species ◽

Transboundary Air Pollution ◽

Local Air Pollution ◽

The City

Transboundary air pollution (TAP) and local air pollution (LAP) influence the air quality of urban areas. Fukuoka, located on the west side of Japan and affected by TAP from the Asian continent, is a unique example for understanding the contribution of LAP and TAP. Gaseous species and particulate matter (PM) were measured for approximately three weeks in Fukuoka in the winter of 2018. We classified two distinctive periods, LAP and TAP, based on wind speed. The classification was supported by variations in the concentration of gaseous species and by backward trajectories. Most air pollutants, including NOx and PM, were high in the LAP period and low in the TAP period. However, ozone was the exception. Therefore, our findings suggest that reducing local emissions is necessary. Ozone was higher in the TAP period, and the variation in ozone concentration was relatively small, indicating that ozone was produced outside of the city and transported to Fukuoka. Thus, air pollutants must also be reduced at a regional scale, including in China.

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