Air Quality Monitoring Data Research Articles

LOF 이상치 알고리즘을 활용한 국내 초미세먼지 발생 특성 탐지 분석

Air quality management is significant to guarantee public health and ecosystem. For the effective control and management of atmospheric environment through different forms of approaches, analyzing of monitoring data and emission inventory should be firstly considered. Air monitoring stations and their real-time data play important roles to understand pollution circumstance around certain region. However, many of atmospheric researchers frequently face difficulties to overview large volumes of monitoring data, because it requires a lot of time and efforts. Local outlier factor (LOF) broadly applied in different research fields is useful data analysis technique to discover particular values have different pattern or/and characteristic in a data group. The authors in this paper has attempted to apply the LOF algorithm to one-year of air quality monitoring data covers the whole Korean territory for suggesting easy way to preliminarily identify the high-level of PM episodes as their occurrence causes i.e. yellow dust, non-yellow dust and domestic wildfire events. As a result, it was effective to figure out particular LOF ranges as the high-level of PM2.5 episodes, because LOF values show comparatively high numbers when concentrations of PM2.5 are rapidly increased or decreased due to LOF algorithm characteristic. The attempt of this research has found the LOF algorithm would be useful to apply as preliminary methodology for understanding which certain episode has been occurred in tremendous data group. Therefore, it would bring further consideration for effectively analyzing the environmental big data in order to establish the air quality management strategy to environmental scientists and policy makers.

Increased average annual prevalence of upper respiratory tract infection (UTRI) in the central Indian population residing near the coal-fired thermal power plants

People are vulnerable to health risks due to particulate matter generated through the coal combustion processes. The air pollution due to the thermal power plant is a primary concern among all sources of pollution. The air pollution due to the coal-fired thermal power plant is a primary concern among all the different sources of pollution. The air quality (suspended particulate matter; SPM) modeling in the study area of central India was carried out using CALAUFF model. In addition, real-time air monitoring of particulate matter PM1, PM2.5 and PM10 was carried out in the study area. Real-time air quality monitoring data showed higher concentration of particulate matter (PM1 and PM2.5) at different locations in the study area, exceeding the regulatory limits set by NAAQS (National Ambient Air Quality Standards) and WHO (World Health Organization). Considering the most probable health impacts due to coal-fired thermal power plant, diseases such as chronic upper respiratory tract infections (URTI), and asthma were focused in this study. Hospital admission data were collected for respiratory disorders from six different public health centers (PHCs) located in the study area for the years 2012 and 2013. Average annual prevalence (AAP) of asthma at Dhapewada, Patansaongi, Chicholi, Satak, Droli and Kanhan PHCs was observed to be 0.581, 0.218, 0.201, 0.155, 0.377 and 0.198%, respectively, whereas AAP of UTRI at Dhapewada, Patansaongi, Chicholi, Satak, Droli and Kanhan PHCs was 24.961, 40.693, 0.769, 12.775, 28.605 and 14.898%, respectively. Thus, we conclude that the study population residing nearby the coal-fired thermal power plants may have an increased risk to upper respiratory tract infections (URTI) than asthma.

Analysis on the Characteristics and Cause of a Compound Pollution Process in Changsha in 2017

Using the conventional meteorological observations, the air Air quality monitoring data, the L-band wind-profiling radar data, the NECP reanalysis data, and the simulation results of the HYSPLIT4 model, the compound pollution process from November 2 to 5, 2017 in Changsha is comprehensively analyzed in this study, including the sources and transport paths of pollutions, the Meteorological conditions and atmospheric boundary layer characteristics. The results showed that the process of air pollution in Changsha is a compound pollution process with high concentration of O3 and fine PM2.5 coexisting. It reached serious pollution from 08:00 to 11 : 00 on 4, PM2.5 was 269.3 ug·m−3 at 09:00 on 4 November. The characteristics of the two phases of this pollution process were clearly different. The first phase was mainly result of accumulation of local pollutants, and the second one was mainly result of outside resource. The maximum wind were light breezein on this pollution process, gentle breeze mainly occurred in the second stage of pollution, and the maximum wind velocity was 4.4m/s. It appearred multi-layer inversion usually, temperature stratification was stabled, and the inversion layer thickness reached the maximum value 909m at 20:00 on 3 November. The boundary layer convergenced and ascented movement, horizontal transport, vertical wind field distribution and atmospheric layer provided the favorable conditions for the air pollution process. The results showed that short-range transport of pollutants is the maily reason based on the HYSPLIT4 model, the northeast airflow was dominant at 100-1000m height. The vary of wind profile data and CO and SO2 emission ratio showed that the different pollution characteristics of two phases, the maximum ratio was more than 100. It has an important indicator for weather forecast in Changsha.

Study on Temporal and Spatial Evolution Characteristics of Air Quality in Urban Agglomerations in Northwestern China

This paper analyzes the air quality monitoring data of Qingyang, Tianshui and Pingliang in northwestern China in 2015 by time series analysis. The research results are as follows: (1) the concentrations of PM2.5, PM10 and SO2 in the three cities began to decline in March, reached a minimum in September before rising gradually; (2) concentrations of PM2.5, PM10 and SO2 in the three cities were low in summer and autumn, but high in winter and spring; (3) the coefficient of correlation among PM2.5, PM10 and SO2 in the three cities was greater than 0.589, indicating a strong correlation.

Analysis of short-term air quality monitoring data in a coastal area

<abstract> <p>Short-term air quality monitoring in a coastal area, Naklua Subdistrict, Pattaya, Thailand is an activity to support the designated area under Thailand's sustainable tourism development. This study provided a short-term monitoring data analysis on time series and Bivariate Polar Plot (BVP) to provide the status of air quality and to determine the potential source area of air pollution. The result showed that NO<sub>2</sub>, SO<sub>2</sub>, CO and PM<sub>10</sub> were not higher than the national air quality standards, while the 24-hour average of PM<sub>2.5</sub> and the 8-hour average of O<sub>3</sub> were slightly higher than the World Health Organization (WHO) air quality guideline values. The nighttime PM<sub>2.5</sub> concentration was higher than the daytime concentration, and its potential source area is urban areas in the south. However, the daytime O<sub>3</sub> concentration is higher than the nighttime concentration. Its potential source area is from the northwest, where Sichang island is located. This result could be used to support air pollution management by controlling and reducing emissions in the potential source areas as the first priority. Also, the study revealed that the BVP technique could be used to determine the source area of air pollution in the coastal area, where wind circulation is more complex than that over the land.</p> </abstract>

Fusion of land use regression modeling output and wireless distributed sensor network measurements into a high spatiotemporally-resolved NO2 product.

Land use regression modeling is a common method for assessing exposure to ambient pollutants, yet it suffers from very coarse temporal resolution. Wireless distributed sensor networks (WDSN) is a promising technology that can provide extremely high spatiotemporal pollutant patterns but is known to suffer from several limitations that put into question its data reliability. This study examines the advantages of fusing data from these two methods and obtaining high spatiotemporally-resolved product that can be used for exposure assessment. We demonstrate this approach by estimating nitrogen dioxide (NO2) concentrations at a sub-urban scale, with the study area limited by the deployment of the WDSN nodes. Specifically, hourly-resolved fused-data estimates were obtained by combining a stationary traffic-based land use regression (LUR) model with observations (15min sampling frequency) made by an array of low-cost sensor nodes, with the sensors' readings mapped over the whole study area. Data fusion was performed by merging the two independent information products using a fuzzy logic approach. The performance of the fused product was examined against reference hourly observations at four air quality monitoring (AQM) stations situated within the study area, with the AQM data not used for the development of any of the underlying information layers. The mean hourly RMSE between the fused data product and the AQM records was 9.3ppb, smaller than the RMSE of the two base products independently (LUR: 14.87ppb, WDSN: 10.45ppb). The normalized Moran's I of the fused product indicates that the data-fusion product reveals more realistic spatial patterns than those of the base products. The fused NO2 concentration product shows considerable spatial variability relative to that evident by interpolation of both the WDSN records and the AQM stations data, with significant non-random patterns in 74% of the study period.

Air Pollutants Reductions During and after the COVID-19 Lockdown and Their Impact on Mortality in China

Background: Strict lockdown measurements implemented during the COVID-19 pandemic have dramatically reduced the anthropogenic-based emissions in China. We aimed to comprehensively describe air pollution during and after the COVID-19 lockdown in China, and to estimate the mortality burden related to the air pollution changes.Methods: We analyzed national air quality monitoring and mortality data to describe the changes of air pollutants, and to estimate the health impact from air pollution changes during and after the lockdown periods in 2020 compared with 2018-2019. Changes in number of deaths and years of life lost (YLL) were used as indicators in the estimations.Findings: The mean air quality index (AQI), PM10, PM2·5, NO2, SO2 and CO concentrations during the lockdown period in 2020 across China declined by 18·2 (21·2%), 27·0μg/m3 (28·9%), 10.5μg/m3 (18·3%), 8·4μg/m3 (44·2%), 13·1μg/m3 (38·8%), and 0·3mg/m3 (27·3%) respectively, when compared to the same periods during 2018-2019 . We observed an increase in O3 concentration during the lockdown by 5·5μg/m3 (10·4%), and a slight decrease after the lockdown by 3·4μg/m3 (4·4%). As a result, there were 51·3 (95%CI: 32·2, 70·1) thousand fewer premature deaths (16·2 thousand during and 35·1 thousand after the lockdown), and 1,066·8 (95%CI: 668·7, 1,456·8) thousand fewer YLLs (343·3 thousand during and 723·5 thousand after the lockdown) than these in 2018-2019 in China.Interpretation: The COVID-19 lockdown has caused substantial decrease in air pollutant concentrations except for O3, which has been accompanied by substantial mortality reductions in China.Funding: Key-Area Research and Development Program of Guangdong Province.Declaration of Interests: We declare no competing interests.Ethics Approval Statement: This study was approved by the Ethics Committee of Guangdong Provincial Center for Disease Control and Prevention (W96-027E-2020004).

Effect of Urban Greening on Incremental PM2.5 Concentration During Peak Hours.

In China, severe haze is a major public health concern affecting residents' health and well-being. This study used hourly air quality monitoring data from 285 cities in China to analyze the effect of green coverage (GC) and other economic variables on the incremental PM2.5 concentration (ΔPM2.5) during peak hours. To detect possible non-linear and interaction effect between predictive variables, a kernel-based regularized least squares (KRLS) model was used for empirical analysis. The results show that there was considerable heterogeneity between cities regarding marginal effect of GC on ΔPM2.5, which could potentially be explained by different seasons, latitude, urban maintenance expenditure (UE), real GDP per capita (PG), and population density (PD). Also described in this study, in cities with high UE, the growth of GC, PG, and PD always remain a positive impact on mitigation of haze pollution. This shows that government expenditure on urban maintenance can reduce or mitigate the environmental pollution from economic development. In addition, the influence of other urban elements on air quality had also been analyzed so that different combinations of mitigation policies are proposed for different regions in this study to meet the mitigation targets.

The Impact of Haze Pollution on Firm-Level TFP in China: Test of a Mediation Model of Labor Productivity

The core issue for China’s economy to shift from high-speed growth to high-quality development is to raise total factor productivity (TFP). Based on the data of A-share listed companies from 2013 to 2017 and the air quality monitoring data released by the China National Environmental Monitoring Centre, this paper conducts an empirical test on the relationship between haze pollution, labor productivity, and firm-level TFP by using the mediation effect test model. The results show the following: First, haze pollution will reduce a firm’s TFP. Second, labor productivity plays a partial intermediary role between haze pollution and a firm’s TFP. Haze pollution will not only have a direct negative impact on a firm-level TFP but also reduce a firm-level TFP by reducing labor productivity. Subsequently, the robustness test was carried out by means of a substitution of independent variables and dependent variables, and the results all supported the hypotheses in this paper. In addition, the heterogeneity of the industry and the ownership of listed companies was analyzed, and the two-stage least squares (2SLS) method was used to solve the endogeneity problem. The conclusions of this paper are helpful to clarify the relationship between haze pollution, firm factor productivity, and firm-level TFP. The paper’s findings also provide a practical basis for firms to further improve TFP from the perspective of air pollution problems such as haze.

Outdoor air pollution and terminal duct lobular involution of the normal breast

BackgroundExposure to certain outdoor air pollutants may be associated with a higher risk of breast cancer, though potential underlying mechanisms are poorly understood. We examined whether outdoor air pollution was associated with involution of terminal duct lobular units (TDLUs), the histologic site where most cancers arise and an intermediate marker of breast cancer risk.MethodsPathologist-enumerated TDLUs were assessed in H&E (hematoxylin and eosin)-stained breast tissue sections from 1904 US women ages 18–75 who donated to the Susan G. Komen Tissue Bank (2009–2012). The 2009 annual fine particulate matter < 2.5 μm in diameter (PM2.5) total mass (μg/m3) at each woman’s residential address was estimated from the Environmental Protection Agency’s Downscaler Model combining Community Multiscale Air Quality (CMAQ) System modeling with air quality monitoring data. We secondarily considered CMAQ-modeled components of PM2.5 and gaseous pollutants. We used K-means clustering to identify groups of individuals with similar levels of PM2.5 components, selecting groups via cluster stability analysis. Relative rates (RRs) and 95% confidence intervals (95% CIs) for the association between air pollutants and TDLU counts were estimated from a zero-inflated negative binomial regression model adjusted for potential confounders.ResultsPM2.5 total mass was associated with higher TDLU counts among all women (interquartile range (IQR) increase, RR = 1.06; 95% CI: 1.01–1.11). This association was evident among both premenopausal and postmenopausal women (premenopausal RR = 1.05, 95% CI: 1.00–1.11; postmenopausal RR = 1.11, 95% CI: 1.00–1.23). We identified 3 groups corresponding to clusters that varied geographically and roughly represented high, medium, and low levels of PM2.5 components relative to population mean levels. Compared to the cluster with low levels, the clusters with both high (RR = 1.74; 95% CI: 1.08–2.80) and medium (RR = 1.82; 95% CI: 1.13–2.93) levels were associated with higher TDLU counts; although not significantly different, the magnitude of the associations was stronger among postmenopausal women.ConclusionsHigher PM2.5 levels were associated with reduced TDLU involution as measured by TDLU counts. Air pollution exposure may influence the histologic characteristics of normal tissue which could in turn affect breast cancer risk.

Air Quality Implications of COVID-19 in California

The COVID-19 pandemic has significantly affected human health and the economy. The implementation of social distancing practices to combat the virus spread, however, has led to a notable improvement in air quality. This study compared the surface air quality monitoring data from the United States Environmental Protection Agency (U.S. EPA)’s AirNow network during the period 20 March–5 May in 2020 to those in 2015–2019 from the Air Quality System (AQS) network over the state of California. The results indicated changes in fine particulate matter (PM2.5) of −2.04 ± 1.57 μg m−3 and ozone of −3.07 ± 2.86 ppb. If the air quality improvements persist over a year, it could potentially lead to 3970–8900 prevented premature deaths annually (note: the estimates of prevented premature deaths have large uncertainties). Public transit demand showed dramatic declines (~80%). The pandemic provides an opportunity to exhibit how substantially human behavior could impact on air quality. To address both the pandemic and climate change issues, better strategies are needed to affect behavior, such as ensuring safer shared mobility, the higher adoption of telecommuting, automation in the freight sector, and cleaner energy transition.

Uncertainty in the Impact of the COVID-19 Pandemic on Air Quality in Hong Kong, China

Strict social distancing rules are being implemented to stop the spread of COVID-19 pandemic in many cities globally, causing a sudden and extreme change in the transport activities. This offers a unique opportunity to assess the effect of anthropogenic activities on air quality and provides a valuable reference to the policymakers in developing air quality control measures and projecting their effectiveness. In this study, we evaluated the effect of the COVID-19 lockdown on the roadside and ambient air quality in Hong Kong, China, by comparing the air quality monitoring data collected in January–April 2020 with those in 2017–2019. The results showed that the roadside and ambient NO2, PM10, PM2.5, CO and SO2 were generally reduced in 2020 when comparing with the historical data in 2017–2019, while O3 was increased. However, the reductions during COVID-19 period (i.e., February–April) were not always higher than that during pre-COVID-19 period (i.e., January). In addition, there were large seasonal variations in the monthly mean pollutant concentrations in every year. This study implies that one air pollution control measure may not generate obvious immediate improvements in the air quality monitoring data and its effectiveness should be evaluated carefully to eliminate the effect of seasonal variations.

An integrated approach for analyzing air quality monitoring data: a case study in Hanoi, Vietnam

In this research, we propose an integrated approach to retrieve the information of air quality monitoring data for air quality assessment. Used data is the hourly concentrations of PM10, PM2.5, SO2, NO, NO2, O3, and CO measured by an ambient air quality monitoring station in Hanoi, Vietnam, in the period of 2010–2018. First, the dataset was preprocessed to remove outliers by a simple removing method. Then, the missing values were filled up using a deep learning technique. After that, the preprocessed data went through the process of analysis to infer the probability distributions. After the probability density functions (pdf) of the hourly concentrations of these air pollutants in the year were determined, the information of air quality data in each year including the annual mean and median, the maximum concentration, and the number of exceedances of hourly thresholds for the period of 2010–2018 was obtained. High level of PM2.5, exceeding its national annual standard, was observed implying the potential of health risk to the people. Trend and temporal variations of these air pollutants were also analyzed. It is found out that the effect of direct emission from local traffic activity on the ambient levels of SO2 and PM2.5 is not dominant.

Particulate matter (PM10 and PM2.5) concentrations during a Saharan dust episode in Istanbul

Istanbul, the biggest city of Turkey, is in a common route for air parcels. Air pollutants are carried over the city from Asian, African, and European continents. Sahara Desert, the largest dust source on earth, affects Turkey’s air qualities substantially due to millions of tons of mineral dust being transported from the African continent towards Turkey every year. Although the effect of Saharan dust transportation on PM10 concentrations in Turkey was examined many times, its effect on PM2.5 concentrations has not been studied yet sufficiently. In February 2015, Istanbul experienced a Saharan dust episode and during this event the concentrations of particulate matter rose to very high levels. This study focuses on particulate matter concentrations (PM10 and PM2.5) during this Saharan dust episode to better understand the effect of dust transportation on Istanbul’s air quality. HYSPLIT trajectory model, satellite products, and air quality monitoring data from ground observations were utilized. We show that the PM10 concentrations increased significantly during the dust episode while PM2.5 concentrations didn’t increase considerably. There was only a slight rise in the values of PM2.5. The significant increase for the PM10 values can be explained by the higher gravitational settling velocities of coarse particles in the atmosphere.

COVID-19 and Air Quality: Evidence from China

ABSTRACT To test the impact of the COVID-19 pandemic on air quality, this article matches the city-level real-time air quality monitoring data with the big data on population migration provided by Baidu. The article uses urban samples from the same data sample of the Chinese lunar calendar in 2019 to construct the counterfactual status of the COVID-19 pandemic. Then, the difference-in-differences (DID) model is employed to estimate the impact of the COVID-19 pandemic on air quality. It is found that the COVID-19 pandemic caused PM2.5 and AQI to decrease by about 7 μg/m3 and 5-points, respectively.

A study on extending the use of air quality monitor data via deep learning techniques

As the environmental awareness of urban citizens increases, traditional air quality monitoring stations cannot satisfy the need for air quality data at high temporal and spatial resolution due to their high construction and maintenance costs. Low-cost air quality monitors are being increasingly used for this purpose because of their portability and affordable price. However, low-cost monitors are usually beset by data quality issues, and the number of mounted air pollutant sensors is limited by the restriction of the cost and size of monitors. Therefore, we propose to extend the use of air quality monitor data via a deep learning technique called long short-term memory (LSTM). The extension is embodied in two aspects: first, calibration of air pollutant concentration data; and second, provision of indicative information about air pollutants where no corresponding sensors are available. A low-cost air quality monitor called KOALA (Knowing Our Ambient Local Air Quality), which was deployed in Sydney (Australia), was used as an example to prove this method’s feasibility. Data from a 90-day period were used for model training, and data from a 30-day period were used for model validation. Random forest models were used for selecting the most useful LSTM model input variables. Historical 24-h information was incorporated to improve the performance of the LSTM models. The results showed that: first, LSTM models can be used to calibrate KOALA carbon monoxide (CO) data with the optimum input being raw CO measurements and the corresponding standard deviation information; and second, LSTM models can be used to estimate ozone concentration with the optimum input being CO concentration and three meteorological parameters [i.e. top soil layer temperature, 10 m U wind (earth-relative), and net shortwave radiation flux at the ground] generated through a deterministic model known as WRF (Weather Research and Forecasting). In addition, the LSTM ozone estimation model showed good performance at both the training location and a location 11 km away, indicating that the proposed method can be used to provide indicative information about air pollutants around the training location.

Poor Air Quality and Its Association with Mortality in Ho Chi Minh City: Case Study

Along with its rapid urban development, Ho Chi Minh City (HCMC) in recent years has suffered a high concentration of air pollutants, especially fine particulate matters or PM2.5. A comprehensive study is required to evaluate the air quality conditions and their health impact in this city. Given the lack of adequate air quality monitoring data over a large area of the size of HCMC, an air quality modeling methodology is adopted to address the requirement. Here, by utilizing a corresponding emission inventory in combination with The Air Pollution Model-Chemical Transport Model (TAPM-CTM), the predicted concentration of air pollutants is first obtained for PM2.5, NOx, and SO2. Then by associating the pollutants exposed with the mortality rate from three causes, namely Ischemic Heart Disease (IHD), cardiopulmonary, and lung cancer, the impact of air pollution on human health is obtained for this purpose. Spatial distribution has shown a high amount of pollutants concentrated in the central city with a high density of combustion vehicles (motorcycles and automobiles). In addition, a significant amount of emissions can be observed from stevedoring and harbor activities, including ferries and cargo handling equipment located along the river. Other sources such as household activities also contribute to an even distribution of emission across the city. The results of air quality modeling showed that the annual average concentrations of NO2 were higher than the standard of Vietnam National Technical Regulation on Ambient Air Quality (QCVN 05: 2013 40 µg/m3) and World Health Organization (WHO) (40 µg/m3). The annual average concentrations of PM2.5 were 23 µg/m3 and were also much higher than the WHO (10 µg/m3) standard by about 2.3 times. In terms of public health impacts, PM2.5 was found to be responsible for about 1136 deaths, while the number of mortalities from exposure to NO2 and SO2 was 172 and 89 deaths, respectively. These figures demand some stringent measures from the authorities to potentially remedy the alarming situation of air pollution in HCM City.

Spatiotemporal Relationships between Air Quality and Multiple Meteorological Parameters in 221 Chinese Cities

Air quality in China is characterized by significant spatial and temporal differences, which are directly related to local meteorological conditions. This study used air quality monitoring data, namely, the air pollution index (API) and air quality index (AQI) between 2005 and 2018, together with meteorological data and identified key meteorological factors that affected the spatial and temporal variation of air quality using a random forest algorithm. The spatial and temporal differences in the threshold values of different meteorological factors affecting the concentrations of PM2.5, PM10, SO2, CO, NO2, and O3 were identified. The AQI has the advantages of facilitating higher index values than the API. The air quality showed an improvement from 2005 to 2018. Wind direction and precipitation were the most important meteorological factors affecting the air quality in northern and southern China, respectively, which to some extent reflected the causes and degradation mechanisms of air pollution in the two regions. There were significant spatial and temporal differences in the effects of meteorological factors on the concentrations of different pollutants. The influence of atmospheric pressure on pollutant concentration differed between the east and west. Precipitation and relative humidity in most cities had significant impacts on PM2.5 and PM10. The influence of relative humidity was most significant for SO2 and it also had a great influence on O3, while wind speed had a great influence on NO2. The results of the study confirm the meteorological sensitivity of air quality and provide support for the implementation of regional air pollution prevention and control initiatives.

Contribution of Secondary Particles to Wintertime PM2.5 During 2015–2018 in a Major Urban Area of the Sichuan Basin, Southwest China

AbstractA novel method, noted as approximate envelope method (AEM), was developed to estimate the secondary PM2.5 concentrations based on the air quality monitoring data. This approach made it possible to obtain the long‐term characteristics of the secondary PM2.5 using only conventional observations. The secondary PM2.5 concentrations and their variation characteristics under different PM2.5 pollution levels during wintertime of 2015 to 2018 in Chengdu, a major urban area of Sichuan Basin over Southwest China, were analyzed. The results showed that the secondary PM2.5 concentrations ranged from 52.3 to 122.8 μg·m−3 and accounted for 56.3%, 63.6%, and 67.4% of the total PM2.5 in slight, moderate, and heavy pollution status, respectively. Additionally, the concentrations of secondary PM2.5 increased year by year, while the concentrations of primary PM2.5 performed significant decreasing trends from 2015 to 2018. The diurnal variations of primary and secondary PM2.5 presented a significantly unimodal patterns but with maximum at morning and noon, respectively. Specific meteorological conditions such as high (low) relative humidity and temperature (wind speed and air pressure) aggravated the secondary particulate pollution in wintertime of Chengdu, reflecting an influence of regional climate change over the Sichuan Basin of Southwest China on the urban air pollution.

Discrete-time Markov chain for prediction of air quality index

Together with water and land, air is a fundamental necessity of life. Nevertheless, the ambient air quality is deteriorating around the world because of rapid urbanization and industrialization. The problem of air pollution has become a prominent issue for the public and academia. In fact, the public is more interested in being informed about the possibility of occurrence of air pollution episodes than the accurate forecasting of a specific pollutant. Therefore, this study proposes a process based upon discrete-time Markov chains (DTMC), to predict the air quality index (AQI) and identify the prime air pollutants in a specific area. This study utilizes online air quality monitoring data retrieved from the Taiwan Environment Protection Administration, to demonstrate the application of the process. The findings of the study revealed that there are three prime air pollutants, namely ozone (O3), nitrogen dioxide (NO2), and fine particulate matter (PM10), which frequently contaminate the ambient air in Taipei city. Furthermore, this study used data for three time periods to verify the proposed process and found that the performance of the process in predicting the AQI values for 7 days is better than the prediction for 30 days and 62 days.