PM10 Data Research Articles

Dispersion modeling of PM10 from selected flow stations in the Niger Delta, Nigeria: implications on soot pollution

BackgroundGas flaring in the Niger Delta releases particles which are dispersed over a wide area and have impacts on the environment and human health. The study aimed at assessing the extent of dispersion of PM10 emitted from gas flares in flow stations. Eight selected flow stations in Rivers and Bayelsa states were investigated. The concentrations of PM10 emitted from the flare stacks were monitored 60 m away from the flare stack using a hand-held Met One AEROCET 531 combined Mass Profiler and Particle Counter. Meteorological parameters such as wind speed, ambient temperature and relative humidity were monitored during the sampling campaign. PM10 and meteorological data were analysed for simple and descriptive statistics using SPSS for Windows (version 21.0). Hybrid Single Particle Lagrangian Integrated Trajectory Model (HYSPLIT) was adopted to predict the dispersion of PM10 from the flow stations.ResultsResults revealed the range concentrations of PM10 from the flow stations (FS 1–8) as 19.9 µg/m3 at FS 1 to 55.4 µg/m3 at FS 8. The maximum concentration of PM10 at FS 8 was higher than the World Health organisation limit of 50 µg/m3. The dispersion of PM10 emitted from FS 1, 4 and 7 in April 2017, had a fitting spread over Port Harcourt City.ConclusionsThe modeling results revealed dispersion of PM10 from the flow stations to 14 states in Nigeria. This suggests possible detrimental health and environmental effects of PM10 on residents in the identified states.

Drivers of severe air pollution events in a deep valley during wintertime: A case study from the Arve river valley, France

The Arve river valley airshed in the French Alps experiences particularly severe air pollution during wintertime stable atmospheric conditions associated with persistent cold-air pools. PM10 data recorded in the region indicate that the urbanised area of the central basin-shape section of the valley is generally the most polluted, with a harmful impact on the health of inhabitants. In the present work, we examine the air pollution transport potential of the Arve river valley airshed using results from high-resolution numerical simulations of a cold-air pool documented as part of the Passy-2015 field campaign. Passive tracers were used to model PM10 with emissions provided by a detailed inventory developed by the local air-quality agency. The observed differential in PM10 levels between valley sections was well captured by the numerical model and could not be explained solely by the differential in emissions. The stagnation, recirculation and ventilation potential of the airshed was evaluated spatially and temporally using integral quantities. The analysis indicated that the central basin-shape section of the valley is poorly ventilated and hence air pollution there would originate mostly from local emission sources. This stagnation zone appears to be almost decoupled from the rest of the airshed. The airshed was decomposed in separate valley sections so as to quantify the fate of the pollutants emitted within each section. Air pollution apportioned according to the contribution of emissions from the different valley sections shows that indeed the central basin-shape section is dominated by local sources. The situation was found more complex in the valley sections further downstream, where the contribution from the sum of the non-local sources can be as large as that from local sources. This study allows to identify the origin of the strong pollution in the Arve river valley, through the link between the local topography, emission sources and pollutant transport.

COVID-19 salgını sırasında Kayseri'nin hava kalitesi ve eşdeğer CO2 değişiminin değerlendirilmesi

The focus of this paper is to evaluate the air quality change of Kayseri Province during the Covid-19 outbreak and examine the possible climate change impact. Air quality were evaluated using PM10, SO2, air temperature, and natural gas consumption data for 56-day quarantine period in Kayseri. Analysis of variance (ANOVA) applied to all data set and then the Tukey-Kramer procedure implemented. CO2 equivalent calculated with EPA (United States Environmental Protection Agency) formulas. The PM10 concentration of 2020 decreased by 40% and 9% in the city center and organized industrial zone (OIZ), respectively compared to 2019. As for SO2, 34% and 30% decreased was seen for the city center and OIZ, respectively. CO2 emission from transportation and natural gas decreased by 5.5 times and 16% than normal periods, respectively. Total reducing CO2 emission during 56-day pandemic period was almost 1 million ton.

MultiStep Ahead Forecasting for Hourly PM10 and PM2.5 Based on Two-Stage Decomposition Embedded Sample Entropy and Group Teacher Optimization Algorithm

The randomness, nonstationarity and irregularity of air pollutant data bring difficulties to forecasting. To improve the forecast accuracy, we propose a novel hybrid approach based on two-stage decomposition embedded sample entropy, group teaching optimization algorithm (GTOA), and extreme learning machine (ELM) to forecast the concentration of particulate matter (PM10 and PM2.5). First, the improvement complementary ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) is employed to decompose the concentration data of PM10 and PM2.5 into a set of intrinsic mode functions (IMFs) with different frequencies. In addition, wavelet transform (WT) is utilized to decompose the IMFs with high frequency based on sample entropy values. Then the GTOA algorithm is used to optimize ELM. Furthermore, the GTOA-ELM is utilized to predict all the subseries. The final forecast result is obtained by ensemble of the forecast results of all subseries. To further prove the predictable performance of the hybrid approach on air pollutants, the hourly concentration data of PM2.5 and PM10 are used to make one-step-, two-step- and three-step-ahead predictions. The empirical results demonstrate that the hybrid ICEEMDAN-WT-GTOA-ELM approach has superior forecasting performance and stability over other methods. This novel method also provides an effective and efficient approach to make predictions for nonlinear, nonstationary and irregular data.

PM <sub>2.5</sub> Decadal Data in Cold vs. Mid-Climate Airports: COVID-19 Era and a Call for Sustainable Air Quality Policy

Airports are identified hotspots for air pollution, notably for fine particles (PM2.5) that are pivotal in aerosol-cloud interaction processes of climate change and human health. We herein studied the field observation and statistical analysis of 10 years data of PM2.5 and selected emitted co-pollutants (CO, NOx, and O3), in the vicinity of three major Canadian airports, with moderate to cold climates. The decadal data analysis indicated that in colder climate airports, pollutants like PM2.5 and CO accumulate disproportionally to their emissions, in fall and winter, in comparison to airports in milder climates. Decadal daily averages and standard errors of PM2.5 concentrations were Vancouver: 5.31 ± 0.017, Toronto: 6.71 ± 0.199, and Montreal: 7.52 ± 0.023 μg/m3. The smallest and the coldest airport with least flights/passengers had the highest PM2.5 concentration. QQQ-ICP-MS/MS and HR-S/TEM analysis of snow near Montreal airport indicated a wide range of emerging contaminants (Cd, Mo, Co, As, Ni, Cr, and Pb) ranging from 0.90 to 622 μg/L, which were also observed in the atmosphere, hinting air-snow interactions. The concentration of selected emerging contaminants in snow near the Montreal airport were at pare with highly polluted snow samples like Alberta’s oil sands regions. The mean freezing temperature of snow close to the airport was -6.19 ± 0.06 °C, statistically higher than in downtown Montreal (-13.35 ± 0.06 °C) hinting to the importance of anthropogenic particles on snow microphysics. During the lockdown a pronounced decrease in the concentrations of PM 2.5 in residential areas close to airports was observed, conforming with the recommended workplace health thresh-holds (~ 2 × 104 cm-3), while before the lockdown condensed nano-sized particles were above the health limit. Targeted reduction of PM2.5 emission is recommended for cold-climate regions.

Association between Exposure to PM <sub>2·5</sub> and the Date-Based Secondary Attack Rate of COVID-19 in Guangzhou, China: A Retrospective Cohort Study

Background: There is no information on the association between exposure to fine particulate matter with an aerodynamic diameter of 2·5 μm or less (PM2·5) and the date-based secondary attack rate (DBSAR) of COVID-19. Methods: In this retrospective cohort study performed in Guangzhou, China, based on hourly concentration data of PM>sub>2·5, PM10, SO2, NO2, and CO from January 1, 2020 to February 15, 2020 from approximately 1,500 monitoring stations in China and the addresses of the participants, we used the Kriging interpolation method to calculate the moving average (MA) concentrations of the pollutants over the previous 1 to 14 days before symptom onset of secondary cases. After simultaneously adjusting for a series of key covariates, we evaluated the dose-response relationship between PM2·5 and DBSAR using a generalized linear mixed model. Findings: DBSAR fluctuated from 2·12% on January 17 to 6·61% on February 13. The significantly positive relationship between PM2·5 exposure and DBSAR at prior 1 to 14 MA days. For example, the dose-response relationship was as follows, for every 10 μg/m 3 increase in PM2·5, DBSAR increased by 2·77% (95% CI: 1·32% to 4·22%; P < 0·001) at prior 1 MA day and 5·82% (95% CI: 4·00% to 7·65%; P < 0·0001) at prior 14 MA days. The effect of PM2·5 exposure in DBSAR significantly differed by age and exposure time. Interpretation: There was a strong nonlinear dose-response relationship between exposure to PM2·5 and DBSAR at prior 1 to 14 MA days, which provide significant evidence and suggests that transmission by aerosol is possible. Funding Statement: This study was supported by grants from the Health Science and Technology Plan Project of Guangzhou (20191A01158). Declaration of Interests: All authors declare no competing interests. Ethics Approval Statement: This study was approved by the ethics committee of the Guangzhou CDC (Guangzhou, China). Analyses involving personally identifiable data were conducted at the Guangzhou CDC. The analyses were performed with anonymized data.

Distribution Characteristics and Influencing Factors of PM2.5 in Northern-Taiwan

This paper analyzes the concentration data of PM2.5, SO2, NO2, CO and O3 recorded at 25 monitoring points in northern Taiwan in 2015. The geographical distribution and seasonal changes of PM2.5 are assessed. The geographical distribution is higher in cities than in rural areas, and higher in the west and the middle than the east. In the seasonal changes, the pollution is the most serious in spring and lightest in autumn. The author uses Pearson correlation coefficient to analyze the correlation between PM2.5 and gaseous pollutants (SO2, NO2, CO and O3). The PM2.5 concentration in Northern-Taiwan Province has a strong correlation with SO2, NO2 and CO, and a weak correlation with O3.

Characterising particulate matter source contributions in the pollution control zone of mining and related industries using bivariate statistical techniques

Na Phra Lan Subdistrict is a pollution control zone with the highest PM10 level in Thailand. Major mobile and industrial sources in the area are related to stone crushing, quarrying and mining. This study used statistical techniques to investigate the potential sources influencing high PM10 levels in Na Phra Lan. Hourly PM10 data and related parameters (PM2.5, PMcoarse and NOx) from 2014–2017 were analysed using time series, bivariate polar plot and conditional bivariate probability function (CBPF). Results of diurnal variation revealed two peaks of PM10 levels from 06:00–10:00 and 19:00–23:00 every month. For seasonal variation, high PM10 concentrations were found from October to February associated with the cool and dry weather during these months. The bivariate polar plot and CBPF confirmed two potential sources, i.e., resuspended dust from mobile sources close to the air quality monitoring station (receptor) and industrial sources of mining, quarrying and stone crushing far from the station on the northeast side. While the industrial source areas played a role in background PM10 concentrations, the influence of mobile sources increased the concentrations resulting in two PM10 peaks daily. From the study results, we proposed that countermeasure activities should focus on potential source areas, resuspended road dust from vehicles and the industrial sources related to quarrying and mining, rather than distributing equal attention to all sources.

Collocated speciation of PM2.5 using tandem quartz filters in northern nanjing, China: Sampling artifacts and measurement uncertainty

In this study, collocated samples of particulate matter with aerodynamic diameter less than 2.5 μm (PM2.5) were collected every sixth day on quartz filters (Qf) at a suburban site in northern Nanjing, China for one year. A backup quartz filter (Qb) was installed behind Qf to estimate positive artifacts. The analysis of gravimetric mass, water-soluble inorganic ions (WSIIs), total organic (OC) and elemental carbon (EC), water-soluble OC (WSOC), total nitrogen (WSTN) and organic nitrogen (WSON) were performed on both Qf and Qb. Due to the high mass loadings on Qf (17.9 ± 7.82 mg filter−1, 4.71–38.5 mg filter−1), the collocated precision of gravimetric mass and dominant species (NH4+, NO3−, SO42−, OC, and EC) is better than that from previous work. Except K+, EC, and WSON, all other target components were detected on Qb with average Qb/Qf ratios ranging from 3.02 ± 3.48–21.7 ± 22.4%. The final concentrations and uncertainties of PM2.5 components were determined based on duplicate Qf–Qb data. The results suggest that using an error fraction of 10% will underestimate the uncertainty of less concentrated species (e.g., Ca2+ and Mg2+) in PM2.5. Due to the evaporation loss of semi-volatile materials from the Qf, the Qf–Qb calculation would lead to an estimate of the lower limit for particulate NH4+, NO3−, and OC. Synchronized hourly data of PM2.5 mass and components were obtained at downtown Nanjing. The comparisons of gravimetric versus reconstructed PM2.5 and filter-based versus continuous measurements of PM2.5 components indicate that a substantial fraction of the unexplained gravimetric PM2.5 can be attributed to aerosol water.

Assessing the Immediate Effect of Covid-19 Lockdown on Air Quality: A Case Study of Delhi, India

Abstract In India, a nationwide lockdown due to COVID-19 has been implemented on 25 March 2020. The lockdown restrictions on more than 1.3 billion people have brought exceptional changes in the air quality all over the country. This study aims to analyze the levels of three major pollutants: particulate matter sized 2.5 μm (PM2.5) and 10 μm (PM10), and nitrogen dioxide (NO2) before and during the lockdown in Delhi, one of the world’s most polluted cities. The data for PM2.5, PM10, and NO2 concentrations are derived from 38 ground stations dispersed within the city. The spatial interpolation maps of pollutants for two times are generated using Inverse Distance Weighting (IDW) model. The results indicate decreasing levels of PM2.5, PM10, and NO2 concentrations in the city by 93%, 83%, and 70% from 25 February 2020 to 21 April 2020 respectively. It is found that one month before the lockdown the levels of air pollution in Delhi were critical and much higher than the guideline values set by the World Health Organization. The levels of air pollution became historically low after the lockdown. Considering the critically degraded air quality for decades and higher morbidity and mortality rate due to unhealthy air in Delhi, the improvement in air quality due to lockdown may result as a boon for the better health of the city’s population.

PM2.5 Exposure in Public Primary Schools Kampala, Uganda

Background Worldwide, 93% of the world’s children under 15 years of age are exposed to ambient fine particulate matter (PM2.5) levels above WHO defined air quality standards of 25 μg/m3 (24-hour mean). In low- and middle-income countries around the world, 98% of all children under 5 are exposed to PM2.5 levels above WHO suggested three interim air quality targets. In comparison, in high-income countries, 52% of children under 5 are exposed to levels above WHO air quality guidelines. We measured daily levels of ambient PM2.5 concentrations, temperature, relative humidity in public primary schools in Kampala City Uganda. Methods We conducted the Ambient PM2.5 Concentration measurements using the E-samplers centrally installed in four public primary schools in divisions of Kawempe, Central, Makindye and Lubaga in Kampala City, Uganda. The E-Samplers generated real-time, data for PM2.5 concentrations, Relative Humidity and Ambient Temperature. Data analysis was done using Excel 2016 to generate daily averages of PM2.5 concentrations for the Month of June 2018. Results The results showed the lowest and highest average daily PM2.5 concentrations for St. Paul Primary School Minimum 17 µg/m3 and Maximum 44 µg/m3); Central - Buganda Rd. Primary School Minimum 14 µg/m3 and Maximum 45 µg/m3); Makindye - Kibuli Demonstration Primary School Minimum 9 µg/m3 and Maximum 46 µg/m3); Rubaga - Queen of Peace Primary School Minimum 9 µg/m3 and Maximum 110 µg/m3). Studies have shown that exposure to high levels of PM2.5 concentrations results increased morbidity and mortality from cardiovascular and respiratory conditions. Conclusions There was an observed increase in ambient PM2.5 Concentration beyond World Health Organization defined limit of 25 μg/m3 and US EPA 35 μg/m3 (24-hour mean). Funding Sources: NIH Fogarty International Center, NIEHS, CDC/NIOSH, Canada’s IDRC, GACC Grant # 5R24 TW009552 [AAU]; 5R24 TW009548 [USC]; 1U01TW010094 [AAU]; 1U2RTW010125 [USC]

Effect of housewife's activity pattern on indoor particulate matter concentration: Ko-CHENS Mom study

Background/Aim Most housewives spend a lot of time at home and do housework such as cleaning and cooking. Low-cost sensors have advantage of being able to measure indoor particulate matter concentration in real time. Activities such as cooking, eating and cleaning can affect exposure patterns of indoor particulate matter. The purpose of this study was to identify changes in indoor particulate matter concentration according to the time-activity patterns of housewives. Methods We enrolled 306 participants in the study for the housewives located in Seoul and Ulsan, Korea. Housewives were measured their indoor exposures by low-cost sensor and completed time-activity diaries for a 7-days monitoring period. PM data is obtained every 5 minutes via Wi-Fi and cloud system connection. Participants self-reported their major activities every hour in their time-activity diary. The time activity diary provides hourly information on participants' activities and locations. Indoor PM concentrations over time were compared with participants' behavior patterns. We also performed the stratified analysis based on weekdays/weekends and seasons. The accuracy of the sensor measurment was evaluated by examining the correlation between the indoor PM measured by the sensor and the indoor PM measured by the GRIMM. Results During the study, the indoor occupancy rate of the participants was about 82%. The average age of the participants was 34 years. The time having highest average of indoor PM concentration was 10 am (PM2.5: 21.8 μg/m3, PM10: 26.3 μg/m3). Activity patterns related to cooking/eating, and cleaning influenced changes in indoor particulate matter concentrations. There was no difference in indoor PM concentration during weekdays/weekends. The correlation coefficients between concentrations measured by sensor and GRIMM were 0.9. Conclusion This study provides a comprehensive assessment of the variation in indoor particulate matter concentration associated with housewives’ time-activities. Our results found that indoor PM concentrations increased with indoor cooking /cleaning behavior.

Temperature modifies the association between air pollution and cardiovascular disease hospital admissions in the Vaal Triangle Air Pollution Priority Area, South Africa

Background: Very few studies in Africa investigated effect modification by a temperature indicator (e.g. apparent temperature, Tapp) on the human health effects of air pollution. Due to climate change, an increase of 3-4°C in ambient temperature is projected along the South African coast and 6-7°C inland during the next 80 years. Methods: Cardiovascular disease (CVD) hospital admission data (ICD10 I00-I99) were obtained from a private hospital group; after ethics approval. The study period was 1 January 2011 to 31 October 2016. NO2, SO2, PM2.5, PM10, temperature and relative humidity data were obtained from the Department of Environmental Affairs. A case-crossover epidemiological study design was applied. The present study focused on lag0-1; as most other studies. Models were adjusted for public holidays and Tapp. Days with Tapp below and above/equal the 25th (9.6°C) and 75th percentiles (20.1°C) were classified as cold and warm days, respectively. Results: Of the 15 237 admissions, 45.9% were from women and 39.0% from 15-65 year olds. The daily NO2, SO2, PM2.5 and PM10 levels were 30.3, 17.1, 35.4 and 60.8 μm/m3, respectively, and exceeded the WHO guidelines between 25% to 50% of the days. None of the air pollutants had significant associations with CVD admissions on cold or warm days. The elderly appeared to be more at risk to PM10 on normal days compared to all groups combined, e.g. RR 1.037 95%CI 1.009-1.065 per 10 μm/m3 increase compared to the unstratified analyses RR 1.017 95%CI 1.001-1.035. The same was observed for males RR 1.026 95%CI 1.003-1.050. PM2.5 had a stronger association than PM10 among the elderly on normal days: RR 1.068 95%CI 1.008-1.133. Conclusions: These results indicate that the risk of CVD admissions due to ambient air pollution exposure is different on cold, normal and warm days in Cape Town.

Temperature modifies the association between air pollution and respiratory disease mortality in Cape Town, South Africa

Background: Due to climate change, an increase of 3-4°C in ambient temperature is projected along the South African coast and 6-7°C inland during the next 80 years. Very few studies in Africa investigated effect modification by a temperature indicator (e.g. apparent temperature, Tapp) on the human health effects of air pollution. Methods: Respiratory disease (RD) mortality data (ICD10 J00-J99) were obtained from Statistics South Africa; after ethics approval. The study period was 1 January 2006 to 31 October 2015. NO2, SO2, PM10, temperature and relative humidity data were obtained from the City of Cape Town municipality. A case-crossover epidemiological study design was applied. The present study focused on lag0-1; as most other studies. Models were adjusted for public holidays and Tapp. Days with Tapp below and above/equal the 25th (12.6°C) and 75th percentiles (20.3°C) were classified as cold and warm days, respectively. Results: Of the 20376 RD deaths, approximately equal numbers were from females, males, 15-64 and 65+ year olds. The daily NO2, SO2, and PM10 levels were 16.0, 9.2, and 29.9 μm/m3, respectively. Daily air pollutant levels regularly exceeded the WHO guidelines. In general, none of the air pollutants had significant associations with RD mortality on cold days. The younger age group seemed to be more vulnerable to NO2 and PM10 on normal days compared to the elderly. On warm days, females were more susceptible to PM10, e.g. RR 1.065 95%CI 1.001-1.133 compared to the unstratified analyses RR 1.034 95%CI 0.991-1.078. On warm days, the 15-64 year old group were more vulnerable to NO2 and SO2. Conclusions: These results indicate that the risk of RD deaths due to ambient air pollution exposure is different on cold, normal and warm days in the Vaal Triangle Air Pollution Priority Area.

Regional policy synergy and haze governance-empirical evidence from 281 prefecture-level cities in China.

The spatial spillover effect of haze pollution makes the local independent governance model inefficient, and it requires regional synergy to achieve the relatively high efficiency of haze governance. This article counts the frequency of environmental policy vocabulary in the government work reports of 30 provinces in China (mainly include the words ecology, pollution, energy, atmosphere, low carbon, etc.) and multiplies it with the proportion of the added value of the secondary industry in the GDP of the 281 prefecture-level cities under the jurisdiction of each province to obtain quantitative policy information and then use gray relational analysis to measure the degree of policy synergy of 281 prefecture-level cities in China. Finally, based on the data of PM2.5 and the degree of policy synergy of 281 prefecture-level cities in China from 2007 to 2016, combined with the data of a series of urban characteristic variables, the dynamic panel model is used to empirically examine the impact of regional policy synergy on haze governance. The results of the study show that between 2007 and 2016, the level of haze pollution is relatively high, ranging from 30 to 65μg/m3, with the highest level in the central and eastern regions, ranging from 50 to 65μg/m3, and relatively low in the northeast and west, ranging from 30 to 50μg/m3. The degree of policy synergy among China's regions has been volatile during the evolution and is between primary synergy and mild imbalance. The degree of policy synergy has a significant negative impact on haze pollution, and the impact coefficient is about - 0.3; that is, policy synergy has a significant positive effect on haze governance. In addition, the industrial structure with a high proportion of secondary industry, the agglomeration of the population, and the production activities it brings will increase the haze pollution in China, and the increase in government expenditure on science and technology will help alleviate the haze pollution. The government can strengthen regional policy synergy, improve the mechanism of synergistic governance in various regions, and formulate strict and unified standards of regional environmental management based on the conditions of each region.

Prediction models using outdoor environmental data for real-time PM10 concentrations in daycare centers, kindergartens, and elementary schools

Children spend a considerable amount of time in daycare centers, kindergartens, and elementary schools. Poor indoor air quality (IAQ) in the educational facilities can affect the health of the children and impair their academic performance. The prediction of real-time PM10 concentration could be useful to intervene the problem of poor IAQ. This study developed models to predict real-time indoor PM10 concentration in the daycare centers, kindergartens, and elementary schools using outdoor environmental data. Indoor PM10 concentrations were measured in 54 daycare centers, 12 kindergartens, and 21 elementary schools in Seoul, South Korea, using a real-time monitor (AirGuard K) over a period of one year. Multiple linear regression models were used to predict real-time indoor PM10 concentration in these educational facilities using outdoor PM10 and meteorological data as input variable. Four formations (original, ratio of indoor-to-outdoor, root-transformation, and log-transformation) for dependent variable were compared to determine the best performance of the model. A 10-fold cross-validation method was used to evaluate the accuracy of the prediction models. Daycare centers showed the highest indoor PM10 concentration. Root-transformed models with high accuracy were developed to predict the real-time indoor PM10 concentration in educational facilities every 10 min. The R2 of the prediction models were 0.64 in the daycare centers, 0.45 in the kindergartens, and 0.43 in the elementary schools. The 24 h profile of the predicted indoor PM10 was similar to the measured PM10 concentration. The prediction models could provide real-time PM10 levels in educational facilities without direct indoor measurement and observation.

A new fuzzy approach in risk assessment with process capability for air pollution

ABSTRACT Today, the reasons such as the development of technology and industry, the increase in the population and the number of vehicles cause air pollution to increase constantly. Many different components cause air pollution, and increased air pollution poses serious risks to both human health (respiratory distress, eye diseases, physical distress, cancer, etc.) and nature (acid rains). In this study, a new risk analysis approach has been applied for the risks that may occur by considering the component data of SO2, NO2, CO, O3, and PM10 causing air pollution from the measurement stations in Ankara, the capital city of Turkey. In this new approach, the integrated risk analysis (RPN with Cp) method has been developed by calculating the Cp values that take into account the spread for the parameter values of the stations. According to the risk assessment results, the most critical hazards were revealed for each component and a fuzzy model was established based on these hazards. With the fuzzy model, risk scores in different ranges are evaluated in a single range and the critical levels of the measurement stations are ranked. According to the results of the study, two of the seven stations are medium-high; two are medium; two of them are medium-low and the remaining one station is placed in the low category. Since the low category is considered as an acceptable risk class in risk analyzes, stations in the medium-high and medium class, which make up 57% of the stations, can be considered critical.

Investigating PM2.5 responses to other air pollutants and meteorological factors across multiple temporal scales

It remains unclear on how PM2.5 interacts with other air pollutants and meteorological factors at different temporal scales, while such knowledge is crucial to address the air pollution issue more effectively. In this study, we explored such interaction at various temporal scales, taking the city of Nanjing, China as a case study. The ensemble empirical mode decomposition (EEMD) method was applied to decompose time series data of PM2.5, five other air pollutants, and six meteorological factors, as well as their correlations were examined at the daily and monthly scales. The study results show that the original PM2.5 concentration significantly exhibited non-linear downward trend, while the decomposed time series of PM2.5 concentration by EEMD followed daily and monthly cycles. The temporal pattern of PM10, SO2 and NO2 is synchronous with that of PM2.5. At both daily and monthly scales, PM2.5 was positively correlated with CO and negatively correlated with 24-h cumulative precipitation. At the daily scale, PM2.5 was positively correlated with O3, daily maximum and minimum temperature, and negatively correlated with atmospheric pressure, while the correlation pattern was opposite at the monthly scale.

A Closer Look at the Role of the Cyprus Low on Dust Events in the Negev Desert

The Negev Desert in Israel is susceptible to frequent atmospheric events of high dust loading which have been linked with negative human health outcomes, including cardiovascular and respiratory distress. Previous research suggests that the highest levels of dust over the region occur during an atmospheric pattern with a cyclone situated over the eastern Mediterranean. This Cyprus Low can bring unsettled weather and strong westerly winds over the Negev. However, while the overall pattern associated with dust events in the Negev Desert is generally well-understood, it remains unclear why days with seemingly similar weather patterns result in different levels of atmospheric dust. Thus, the goal of this study is to better differentiate the atmospheric patterns during dust events over the Negev. Using PM10 data collected in Be’er Sheva, Israel, from 2000 to 2015 in concert with 72-h HYSPLIT back trajectories at three different height levels (surface, 200 m, 500 m), we examine the source region, trajectory groups using a K-Means clustering procedure, and overall synoptic pattern during dust events. Further, we use sea-level pressure data across the region to determine how cyclone strength and location impact dust events in Be’er Sheva. We find that the highest levels of atmospheric dust in the Negev are associated with the Cyprus Low pattern, and air traversing Libya seems to play an especially important role, likely due to the country’s arid surface cover. Cyclone strength is also a critical factor, as lower sea-level pressure results in more severe dust events. A better understanding of the atmospheric features associated with dust events over the Negev Desert will hopefully aid in forecasting these occurrences across the region.

Prediction of Daily PM10 Concentration for Air Korea Stations Using Artificial Intelligence with LDAPS Weather Data, MODIS AOD, and Chinese Air Quality Data

PM (particulate matter) is of interest to everyone because it can have adverse effects on human health by the infiltration from respiratory to internal organs. To date, many studies have made efforts for the prediction of PM10 and PM2.5 concentrations. Unlike previous studies, we conducted the prediction of tomorrow’s PM10 concentration for the Air Korea stations using Chinese PM10 data in addition to the satellite AOD and weather variables. We constructed 230,639 matchups from the raw data over 3 million and built an RF (random forest) model from the matchups to cope with the complexity and nonlinearity. The validation statistics from the blind test showed excellent accuracy with the RMSE (root mean square error) of 9.905 μg/m3 and the CC (correlation coefficient) of 0.918. Moreover, our prediction model showed a stable performance without the dependency on seasons or the degree of PM10 concentration. However, part of coastal areas had a relatively low accuracy, which implies that a dedicated model for coastal areas will be necessary. Additional input variables such as wind direction, precipitation, and air stability should also be incorporated into the prediction model as future work.