Particulate Matter Mass Concentrations Research Articles

High-Precision Microscale Particulate Matter Prediction in Diverse Environments Using a Long Short-Term Memory Neural Network and Street View Imagery.

In this study, we propose a novel long short-term memory (LSTM) neural network model that leverages color features (HSV: hue, saturation, value) extracted from street images to estimate air quality with particulate matter (PM) in four typical European environments: urban, suburban, villages, and the harbor. To evaluate its performance, we utilize concentration data for eight parameters of ambient PM (PM1.0, PM2.5, and PM10, particle number concentration, lung-deposited surface area, equivalent mass concentrations of ultraviolet PM, black carbon, and brown carbon) collected from a mobile monitoring platform during the nonheating season in downtown Augsburg, Germany, along with synchronized street view images. Experimental comparisons were conducted between the LSTM model and other deep learning models (recurrent neural network and gated recurrent unit). The results clearly demonstrate a better performance of the LSTM model compared with other statistically based models. The LSTM-HSV model achieved impressive interpretability rates above 80%, for the eight PM metrics mentioned above, indicating the expected performance of the proposed model. Moreover, the successful application of the LSTM-HSV model in other seasons of Augsburg city and various environments (suburbs, villages, and harbor cities) demonstrates its satisfactory generalization capabilities in both temporal and spatial dimensions. The successful application of the LSTM-HSV model underscores its potential as a versatile tool for the estimation of air pollution after presampling of the studied area, with broad implications for urban planning and public health initiatives.

КАЧЕСТВО ВОЗДУХА В МОСКВЕ ВО ВРЕМЯ КАРАНТИНА COVID-19 В СРАВНЕНИИ С ДРУГИМИ РЕГИОНАМИ МИРА

An analysis of the changes in the carbon monoxide (CO) and particulate matter (PM10) mass concentrations in the atmosphere near highways and residential areas over Moscow during the spring lockdown in 2020 against of the mass concentrations in the previous years (2015-2019) is presented. In the lockdown period, a decrease in the CO mass concentrations was about 40% near highways and only 10% in residential areas. A decrease in the PM10 mass concentrations by about 10% was found during that period. The closeness of lockdown effects in Moscow and other megacities is shown by the comparison of trends in the traffic intensity and pollutant mass concentrations. The special importance of motor vehicles as a source of carbon monoxide emissions in the analyzed megacities is demonstrated.

In-vehicle ultrafine and fine particulate matter exposures during commuting in a South Asian megacity: Dhaka, Bangladesh

We measured ultrafine particle number concentration (PNC) and fine particulate matter mass concentration (PM2.5) levels in public buses and private cars under three conditions: windows open, windows closed with air recirculation off, and windows closed with air recirculation on in Dhaka, Bangladesh—an extremely polluted megacity in South Asia. We collected measurements on ∼10 repeated round trips on unique days for each selected transportation mode, covering various routes that spanned multiple arterial roads over several months (January to March 2020 and November to December 2020) and different times of the day to account for the variability in traffic and meteorological conditions. We used a condensation particle counter (CPC) to measure PNC and a PurpleAir sensor to measure PM2.5 mass concentrations. We applied corrections to the raw data and analyzed quality-assured data to investigate exposure differences across modes, pollutants, locations, and times. Public buses and private cars with open windows had mean PNC levels of 112,000 # cm−3 and 117,000 # cm−3, approximately 5–6 times higher than the mean urban background level of around 20,000 # cm−3. PM2.5 levels in these vehicles were approximately ∼100 μg m−3, about 20% higher than the urban background PM2.5 level of 85 μg m−3. With closed windows and air recirculation off, concentrations were 15% lower for PNC and 10% lower for PM2.5. Under closing windows and keeping air recirculation on, mean PNC and PM2.5 levels were 60,000 # cm−3 and 70 μg m−3, respectively, representing a 50% reduction for PNC and a 30% reduction for PM2.5 compared to open windows. Our findings suggest that commuters in public buses and private cars with open windows have higher and similar exposure levels, whereas those commute in private cars with closed windows and air recirculation on have lower exposure levels.

Variability and relationships between the size, composition and optical properties of suspended particulate matter in the coastal waters of western Spitsbergen, assessed through measurements of size-fractionated seawater samples

Measurements of inherent optical properties (IOPs) and characteristics of concentration and composition of suspended particles were made on original and size-fractionated surface water samples from Arctic fjords and coastal waters of western Spitsbergen in the Svalbard archipelago, in the summer months of 2021 and 2022. Optical measurements included the spectral scattering coefficient of particles, and spectral absorption coefficients of particles as well as depigmented (non-algal) particles and phytoplankton. Assemblages of suspended particles were characterised by measuring the mass concentrations of suspended particulate matter (SPM), particulate organic matter (POM), particulate inorganic matter (PIM), and phytoplankton pigments including chlorophyll a (Chla). All measurements were performed on original (unfiltered) seawater and on size-fractionated samples obtained by filtration using a combination of nylon meshes and membrane filters. This allowed us to determine the contribution of the fractions of very small (VS), small (S) and combined medium and large particles (ML) to the total SPM and Chla, as well as to the total scattering and absorption coefficients. The obtained results: (i) indirectly indicate a clear variability in particle size distributions occurring in the studied marine environment (e.g., the contribution of ML size fraction to SPM (the ratio SPMML/SPM) varied between 0.10 and 0.52); (ii) indicate noticeable differences in composition between size fractions (e.g., the POM/SPM ratio was on average 0.21 for the S fraction, and 0.34 and 0.32 for the VS and ML fractions, respectively); (iii) in most cases indicate that the fraction S had the largest contribution to all analysed spectral optical coefficients, followed by the VS and ML fractions (the average contributions of the S fraction to scattering coefficient of particles and absorption coefficient of particles or depigmented (non-algal) particles were above 0.6 in the entire analysed spectral ranges); (iv) allowed for the identification of statistical relationships between selected characteristics describing changes in particle size and variability of particle IOPs (e.g., we observed statistical relations between SPMML/SPM and the spectral slope of scattering coefficient by particles, as well as SPM-specific coefficients of scattering by particles).

Impact of atmospheric particulate matter retention on physiological characters of five plant species under different pollution levels in Zhengzhou.

Atmospheric particulate matter (PM) pollution has become a major environmental risk, and green plants can mitigate air pollution by regulating their enzymatic activity, osmoregulatory substances, photosynthetic pigments, and other biochemical characteristics. The present investigation aims to evaluate the mitigation potential of five common evergreen tree species (Photinia serrulata, Ligustrum lucidum, Eriobotrya japonica, Euonymus japonicus, Pittosporum tobira) against air pollution and to assess the effect of dust retention on plant physiological functions exposed to three different pollution levels (road, campus, and park). The results found that the amount of dust retained per unit leaf area of the plants was proportional to the mass concentration of atmospheric particulate matter in the environment, and that dust accumulation was higher on the road and campus than in the park. There were significant differences in dust retention among the five tree species, with the highest leaf dust accumulation observed for E. japonica (5.45 g·m-2), and the lowest for P. tobira (1.53 g·m-2). In addition, the increase in PM adsorption by different plants was uneven with increasing pollution levels, with significant decreases in chlorophyll content, photosynthetic and transpiration rate. From a physiological perspective, P. tobira exhibited greater potential to respond to PM pollution. Biochemical indicators suggested that PM pollution caused changes in plant protective enzyme activities, with a decrease in superoxide dismutase (SOD) and peroxidase (POD) activities, as well as promoting membrane lipid peroxidation, and appropriate stress also enables plants to counteract oxidative damage. In particular, PM exposure also induced stomatal constriction. Overall, PM retention was significantly associated with physiological and photosynthetic traits. In conclusion, our study contributes to the understanding of the effects of PM on plant physiology. Furthermore, it also provides insights into the selection of plants that are tolerant to PM pollution.

Characteristics of airborne particles in stone quarrying areas: Human exposure assessment and mitigation

This investigation aims to assess the levels of human exposure to airborne particulate matter (PM) in various locations of a natural stone quarry for the first time based on simultaneous measurements of both PM mass and number concentrations (PMC and PNC). A quarry located in Danang city, Vietnam, considered to be a “hotspot” of air pollution in the city, was selected for detailed investigations. Both PMC and PNC were found to be significantly higher (1.2–6.0 times) within the quarry compared to surrounding areas. Mechanical activities during mining, notably crushing, screening, hauling, and loading stones, contributed to increased emissions of PM in the coarser mode (1–10 μm) compared to the accumulation mode (0.1–1 μm) and thus increased deposition of PM1-10 in the human upper respiratory tract. In contrast, combustion activities, especially the diesel engine exhaust from various machines and vehicles used in the quarry, resulted in increased emissions of small particles in the accumulation mode that dominated the PNC and in their deposition in the lower respiratory tract. Simultaneous measurements of PNC and PMC revealed that the PM counts were strongly associated with PM deposition in the alveolar region (accounting for ≈ 76% of total PNC of particles less than 10 μm, N10), while the PM mass concentration was a better indicator of the deposition of PM in the head airway region (≈92% of total PMC of PM10). Overall, this study demonstrates the significance of measuring both PNC and PMC to assess PM exposure levels, regional respiratory doses, and potential health effects associated with human exposure to PM generated from stone quarrying activities. The novelty of this work is the integration of real-time mass and number concentrations of PM over the size range from 20 nm to 10 μm to provide insights into respiratory deposited doses of size-fractionated PM among quarry workers.

Magnetic characteristics of atmospheric particulate matter and its indication of atmospheric pollution during winter in Lanzhou, NW China

In order to elucidate the significance of environmental magnetic methods in monitoring atmospheric particulate matter pollution, this study conducted an analysis of the magnetic and chemical properties of PM10 and PM2.5 samples collected during winter in Lanzhou City. The magnetic measurements revealed significant variations in the saturation isothermal remanent magnetization (SIRM) values between daytime and nighttime, with higher SIRM values during nighttime. The magnetic properties were dominated by low-coercivity ferrimagnetic minerals exhibiting multidomain and pseudo-single domain characteristics. SIRM exhibited a significant correlation with the mass concentration of particulate matter. The correlation indicated that during the day, the SIRM of PM2.5 showed a significant correlation with elements such as Zn, Pb, Cl−, K+, K, and organic carbon (OC). Additionally, the SIRM of PM10 was also related to Mg2+, Ca2+, and some crustal elements. During nighttime, the SIRM of PM10 and PM2.5 was primarily associated with Cl−, K+, K, and OC. By comparing the magnetic and chemical properties of particulate matter under clean and non-clean air conditions, similar trends were observed between SIRM and indicators such as NO3−/SO42−, As/Pb, K+/K, and Ca2+, which are commonly associated with traffic emissions, biomass burning, and construction dust sources. Factor analysis was applied to further investigate the sources of magnetic particles, unequivocally demonstrating that traffic emissions constituted the primary source of magnetic particles in both PM10 and PM2.5 during daytime, whereas biomass burning emerged as the predominant source during nighttime. Consequently, our study highlights the innovative use of magnetic parameters as reliable indicators for assessing atmospheric particulate pollution and discerning the sources of particulate matter.

Aerosol Thermodynamics: Nitrate Loss from Regulatory PM2.5 Filters in California.

Fine particulate matter (PM2.5) mass concentrations reported by regulatory networks are declining across the United States. It is well established that ammonium nitrate contributes substantially to the PM2.5 mass in the western United States, and that Teflon filters commonly used by regulatory monitors are subject to negative mass artifacts due to ammonium nitrate volatilization. This study focuses on the San Joaquin Valley (SJV), an environmental justice (EJ) and agricultural region with persistently poor air quality. The SJV is a serious nonattainment area of PM2.5 National Ambient Air Quality Standards (NAAQS) with substantial nitrate mass concentrations. We explicitly model the chemical thermodynamic equilibrium of the ammonium nitrate-nitric acid systems and quantify volatilization across California as a function of the deliquescence point relative humidity (%DRH). Nitrate loss is estimated at all federal reference method (FRM) and federal equivalent method (FEM) monitors from 2001 to 2021. Nearly 20% of PM2.5 mass is lost from filters in the SJV area, especially during winter and fall when particulate nitrate mass is most abundant. All decadal PM2.5 trends calculated from reported measurements in Kern, Tulare, and Fresno counties in the SJV show greater decline in PM2.5 mass when nitrate loss is accounted for, up to a factor of 20 in Kern county. This suggests PM2.5 mass concentrations reported in regulatory networks are biased low relative to the actual atmospheric burden, notably in an EJ area that lags behind most of the country's air quality improvements.

Seasonal Variations in Chemical Composition and Source Apportionment of Fine Particulate Matter (PM1) in an Urban Site of Jaipur City, Rajasthan

The present study was conducted with an aim to investigate the seasonal variation in mass concentration of fine particulate matter (PM1), their chemical composition, (water and soluble and non-soluble ions and other elements) and gaseous pollutants (SO2, NO2, CO and O3) at an urban site of Jaipur city in India. During summers, pollutants showed a reduction of 18.97%, 41.95%, 32.32% and 20.56% for PM1, NO2, SO2 and CO, respectively. In contrast, O3 showed an increase of 27%. The substantial reduction was also observed in the levels of secondary aerosols SO4 2- (71.15%), NO3-(21.86%), Cl- (65.63%) and K+ (8.16%). The elemental components Al, B, Be, Ca, Cu, Fe, Mn, Ni, and Pb showed a reduction in the range of 15.13 % (Al) to 71% (Cu). On the contrary, an increment was found in the levels of Ag (21.95%), Cd (62.5%), Cr (89.74%), Mg (10.43%) and Na (25.32%). Four factors were extracted by principal component analysis (PCA). In addition to the local sources, three stationary sources have been identified as contributing to the pollution load located in the WNW, NE and ENE direction of the site. It can be concluded that pollutant concentration and chemical composition of any area not only depend on the local emission, but nearby stationary sources and meteorology are significant contributors.

Influence of Ammonium Sulfate and Ammonium Nitrate on the Properties of Ambient PM2.5 in Zhenjiang

Recently, the contribution of inorganic salts (nitrates in particular) to the mass concentration of particulate matter with an aerodynamic diameter of less than 2.5 μm (PM2.5) has been increasing across China. However, it is urgent to understand how the increased inorganic salts affect the crucial properties of PM2.5. Here, we conducted continuous field observations at Zhenjiang Ecology and Environment Protection Bureau from January 1 to December 31, 2021. The mass concentrations of ammonium sulfate[(NH4)2SO4] and ammonium nitrate (NH4NO3) were calculated using different methods. The contributions of (NH4)2SO4 and NH4NO3 to the extinction coefficient, hygroscopic growth, and acidity of PM2.5 were discussed in detail. Our results demonstrated that the mean mass concentrations of (NH4)2SO4 and NH4NO3 during the study period were (6.5±4.5) and (15.0±13.3) μg·m-3, which contributed (20.5±18.2)% and (34.5±18.4)% to the mass concentration of PM2.5, respectively. The total extinction coefficient of PM2.5 was (224.5±194.2) Mm-1, in which NH4NO3 was the largest contributor[(40.1±20.9)%] followed by (NH4)2SO4[(19.1±10.8)%]. (NH4)2SO4 and NH4NO3 were also the dominant contributors to the hygroscopic growth of PM2.5. In particular, NH4NO3contributed from (53.8±13.4)% to (61.6±14.6)% to the aerosol water content of PM2.5 under pollution conditions. Thus, NH4NO3 was a key air pollutant to be targeted for further improving the visibility and air quality in Zhenjiang in the future. However, the reduction in the precursors of NH4NO3 would lead to an increase in aerosol acidity, particularly in the spring and winter seasons. Our results help us understand the evolution of air quality and the related impacts and also provide important information on air quality improvement in Zhenjiang in the future.

Environmental exposure disparities in ultrafine particles and PM2.5 by urbanicity and socio-demographics in New York state, 2013–2020

BackgroundThe spatiotemporal and demographic disparities in exposure to ultrafine particles (UFP; number concentrations of particulate matter (PM) with diameter ≤0.1 μm), a key subcomponent of fine aerosols (PM2.5; mass concentrations of PM ≤ 2.5 μm), have not been well studied. ObjectiveTo quantify and compare the aerosol pollutant exposure disparities for UFP and PM2.5 by socio-demographic factors in New York State (NYS). MethodsAmbient atmospheric UFP and PM2.5 were quantified using a global three-dimensional model of chemical transport with state-of-the-science aerosol microphysical processes validated extensively with observations. We matched these to U.S. census demographic data for varied spatial scales (state, county, county subdivision) and derived population-weighted aerosol exposure estimates. Aerosol exposure disparities for each demographic and socioeconomic (SES) indicator, with a focus on race-ethnicity and income, were quantified for the period 2013–2020. ResultsThe average NYS resident was exposed to 4451 #·cm−3 UFP and 7.87 μg·m−3 PM2.5 in 2013–2020, but minority race-ethnicity groups were invariably exposed to greater daily aerosol pollution (UFP: +75.0% & PM2.5: +16.2%). UFP has increased since 2017 and is temporally and seasonally out-of-phase with PM2.5. Race-ethnicity exposure disparities for PM2.5 have declined over time; by −6% from 2013 to 2017 and plateaued thereafter despite its decreasing concentrations. In contrast, these disparities have increased (+12.5–13.5%) for UFP. The aerosol pollution exposure disparities were the highest for low-income minorities and were more amplified for UFP than PM2.5. DiscussionWe identified large disparities in aerosol pollution exposure by urbanization level and socio-demographics in NYS residents. Jurisdictions with higher proportions of race-ethnicity minorities, low-income residents, and greater urbanization were disproportionately exposed to higher concentrations of UFP and PM2.5 than other NYS residents. These race-ethnicity exposure disparities were much larger, more disproportionate, and unabating over time for UFP compared to PM2.5 across various income strata and levels of urbanicity.

Long-term spatial and temporal evaluation of the PM2.5 and PM10 mass concentrations in Lithuania

Evaluating the spatio-temporal trends of particulate mass concentration is beneficial for appraising the risk to human health and environmental quality. In this study, the long-term observational data of particulate matter mass concentration (PM2.5, PM10), and PM2.5/PM10 ratios' variability of sixteen EPA air pollution monitoring stations in Lithuania over 17 years (2006–2022), were analyzed. A seasonal-trend decomposition procedure using Loess and Mann-Kendall regression with Sen's slope was used to qualify long-term trends and seasonality. The Theil-Sen analysis estimated significant reductions of −2.66, −4.42, and −1.89% per year for PM10, PM2.5, and PM2.5/PM10 ratios after 2013. The possible reasons for these changes are the reduction in liquid fuel consumption, the mitigation of energy sector emissions, as well as Lithuania's positive gross domestic product (GDP) in recent years. The results also revealed a significant increasing trend (+4.48%/year) for PM10 in rural areas, due to increased agricultural emissions. Trend analysis was also applied to different quartiles. The upper and middle values of PM2.5 and the upper values of PM10, responsible for high PM mass concentration, underwent a significant decrease.

Occupational health risk assessment of construction workers caused by particulate matter exposure on construction sites

Construction particulate matter is one of the main environmental impact factors in the construction process. Due to the lack of sufficient awareness and understanding of the potential health effects of particulate matter by project managers and construction workers, the on-site working environment has not been effectively improved for a long time, and construction workers have been exposed to high particulate matter concentration conditions for physical labor for a long time. The construction site is a special operation scene, and the source and diffusion of particulate matter are a complex physical change process, and the degree of damage to the health of construction workers is closely related to the exposure dose. Thus, suitable quantitative and evaluation methods need to be adopted. The current on-site particulate matter concentration control system lacks technical and data support and cannot support the needs of on-site environmental management. In this paper, three construction sites in different stages of construction in Shanghai were selected to measure the mass concentration of open source particulate matter, and on this basis, the emission factors of particulate matter in different operating areas were calculated. At foundation stage, the emission factor of TSP, PM10, PM2.5 are 0.0214 g/m2·h, 0.0067 g/m2·h, 0.0054 g/m2·h; at main structure stage, the emission factor of TSP, PM10, PM2.5 are 0.0136 g/m2·h, 0.0053 g/m2·h, 0.0041 g/m2·h; at installation and decoration stage, the emission factor of TSP, PM10, PM2.5 are 0.0165 g/m2·h, 0.0059 g/m2·h, 0.0043 g/m2·h. Using simulation software to simulate the temporal and spatial distribution of particulate matter concentration at the site of the example project, it is found that workers engaged in pit bottom operation in the foundation stage, steel bar processing in the main structure stage, and plastering, masonry and putty workers in the installation and decoration stage are the people with the highest occupational health risk at the construction site. In this study, DALYs were used as a metric to monetize the health risks of particulate matter to workers in the field. Support scientific decision-making on particulate matter control at construction sites and improve the level of on-site occupational health management.

Physicochemical characteristics of atmospheric particles in south western (Pune) region of India: Elemental size distribution, source analysis and risk assessment

Chemical characteristics of size-resolved particulate matter (PM) and bound elemental species were investigated under an intensive measurement campaign during winter of 2021 in urban dwelling of south western (Pune) region of India. During the study period, average mass concentrations of PM10, PM4, PM2.5 and PM1 were 182.4 ± 48.7 μgm−3, 76.2 ± 26.2 μgm−3, 63.5 ± 22.4 μgm−3, 17.7 ± 6.9 μgm−3 respectively. Diurnal variability indicated significant high concentration during night and early morning hours attributed to the high volume of heavy-duty vehicles. Bimodal distributions of PM mass concentrations in coarse fraction (Mass Median Aerodynamic Diameter (MMAD) ± Geometric Standard Deviation (GSD) = 4.32 ± 1.27) was noted while accumulation of elements was found higher in fine(0.43–2.1 μm) > coarse(2.1–5.8 μm) > large mode(5.8–10 μm) particles. As per size distribution characteristics, Mn, Pb, Cu, Sn, Zn, Mo were concentrated in coarse mode; As, Cr, Fe, Ni was concentrated in both large and coarse mode while Al, Si depicted multimodal distribution across coarse and fine range. High MMAD (≥1.8) and GSD (>1.2) of all elements pointed towards significance of coarse particles in this study region. Ni and Cr (VI) via inhalation exposure pathway pose highest hazard quotient (HQ) and excess lifetime cancer risk (ELCR) respectively within respirable size range(2.1–3.3 μm) while children were subject to potential health risk than adults mainly through ingestion mode of exposure. Principal Component Analysis (PCA) suggested crustal origin, resuspended road dust along with vehicular traffic and industrial emissions as major sources.

Double Inversion Layers Affect Fog–Haze Events over Eastern China—Based on Unmanned Aerial Vehicles Observation

The goal of this work is to analyze how double inversion layers affect fog–haze events and boundary layers over Eastern China based on unmanned aerial vehicle observations. During 10–12 December 2020, two successive fog–haze events occurred in Eastern China. Based on the unmanned aerial vehicle (UAV), wind, temperature, relative humidity (RH), and aerosol mass concentration were monitored simultaneously in Lianyungang, China. Several observations were found after analyzing the physical fields of these data. The results show that the concentrations of air pollutants during fog events were negatively correlated with horizontal visibilities. The mass concentrations of particulate matter increased rapidly (After 08:00 BJT 11) in the fog dissipation stages (PM2.5 and PM10 increased from 97 and 150 μg/m3 to 213 and 300 μg/m3, respectively). Double temperature inversion significantly affected fog events, where the enhancement of the lower-level temperature inversion (where the inversion layer top height was between 150 and 300 m) corresponded to the explosive growth of fog and the dissipation of the upper-level inversion layer. (The bottom height of the inversion layer was between 350 m and 600 m) This indicates the end of fog events. Fog layer thicknesses are negatively correlated with the concentrations of air pollutants in the near-surface layer. The thickening of the inversion layer near the surface corresponds to an increase (after 05:00 BJT 11 and 03:00 BJT 12) in fog layer height. The fog event is maintained when water vapor conditions are favorable enough or when there is a deep near-surface temperature inversion but the maintenance of the near-surface temperature inversion at an intensity of above 2 °C (100 m)−1 mainly contributes to the durations of these two fog events. Calculations through the trajectory model reveal that the air pollutant source varied during these two events, which was the main reason for the difference in air pollutant concentrations between the two events.

Optimal static light scattering detection angle for particulate matter size and concentration measurement

The serious pollution of particulate matter (PM) causes many adverse effects on human health and industrial production. High-precision, real-time monitor of atmospheric particulate concentration is becoming increasingly important. In this study, a method to find the optimal detection angle to measure the mass concentration of PM is proposed. To find an optimal angle, a motorized rotary system is used to measure the light scattering signal of particles from 0° to 180°. The new method obtains parameters such as mass concentration factor, deviation, mass concentration coefficient and deviation coefficient, which can be measured more accurately. Through theoretical calculation and experimental analysis, the optimal detection angle is 45°. At the optimal angle, the correlation between the scattered light intensity and the mass concentration of spherical SiO2 particles reaches 0.9836. Then, the mass concentration of PM is inverted and calculated at the optimal angle. The results showed that the correlation coefficient R2 of different particle sizes is larger than 0.952, and the measurement results were highly correlated.

Laboratory Comparison of Low-Cost Particulate Matter Sensors to Measure Transient Events of Pollution-Part B-Particle Number Concentrations.

Low-cost Particulate Matter (PM) sensors offer an excellent opportunity to improve our knowledge about this type of pollution. Their size and cost, which support multi-node network deployment, along with their temporal resolution, enable them to report fine spatio-temporal resolution for a given area. These sensors have known issues across performance metrics. Generally, the literature focuses on the PM mass concentration reported by these sensors, but some models of sensors also report Particle Number Concentrations (PNCs) segregated into different PM size ranges. In this study, eight units each of Alphasense OPC-R1, Plantower PMS5003 and Sensirion SPS30 have been exposed, under controlled conditions, to short-lived peaks of PM generated using two different combustion sources of PM, exposing the sensors' to different particle size distributions to quantify and better understand the low-cost sensors performance across a range of relevant environmental ranges. The PNCs reported by the sensors were analysed to characterise sensor-reported particle size distribution, to determine whether sensor-reported PNCs can follow the transient variations of PM observed by the reference instruments and to determine the relative impact of different variables on the performances of the sensors. This study shows that the Alphasense OPC-R1 reported at least five size ranges independently from each other, that the Sensirion SPS30 reported two size ranges independently from each other and that all the size ranges reported by the Plantower PMS5003 were not independent of each other. It demonstrates that all sensors tested here could track the fine temporal variation of PNCs, that the Alphasense OPC-R1 could closely follow the variations of size distribution between the two sources of PM, and it shows that particle size distribution and composition are more impactful on sensor measurements than relative humidity.

Healthcare utilization of lung cancer patients associated with exposure to fine particulate matter: A Korean cohort study.

Higher concentrations of particulate matter (PM) have been shown to cause deterioration of the symptoms of respiratory and cardiovascular disease in several regional studies. Here, we aimed to investigate the healthcare utilization of lung cancer patients associated with short-term exposure to PM at the national level in Korea. We extracted the data of 210 558 subjects over a period of 3 years (2015-2017), who were diagnosed with lung cancer before 2015 and benefited from the National Health Insurance Sharing Service. We performed the interpolation method using the geographic information system to calculate the estimated mean PM2.5 and PM10 concentrations by regions and classified three groups as high (upper 10%), intermediate (10%-90%), and low (bottom 10%) based on the mean PM mass concentrations of the month. The monthly average number of outpatient visits was significantly increased in high PM2.5 urban areas (46.296 vs. 50.646, p = 0.015). In high PM2.5 nationwide regions, the monthly average number of emergency admission was significantly increased (0.528 vs. 0.785, p = 0.001). The outpatient visits tended to change with PM2.5 concentration and correlated with PM10 /PM2.5 concentrations in urban and nationwide areas. In high PM2.5 urban regions, there was a significant increase in bronchodilator prescriptions (3.102 vs. 3.758, p = 0.008). Concerning high PM2.5 nationwide regions, there were significantly increased prescriptions of antibiotics, steroids, bronchodilators, antihistamines, and mucolytics. This study suggests that exposure to PM2.5 is significantly associated with hospital utilization and drug prescription in lung cancer patients.

An optimized semi-empirical physical approach for satellite-based PM2.5 retrieval: embedding machine learning to simulate complex physical parameters

Abstract. Satellite remote sensing of PM2.5 (fine particulate matter) mass concentration has become one of the most popular atmospheric research aspects, resulting in the development of different models. Among them, the semi-empirical physical approach constructs the transformation relationship between the aerosol optical depth (AOD) and PM2.5 based on the optical properties of particles, which has strong physical significance. Also, it performs the PM2.5 retrieval independently of the ground stations. However, due to the complex physical relationship, the physical parameters in the semi-empirical approach are difficult to calculate accurately, resulting in relatively limited accuracy. To achieve the optimization effect, this study proposes a method of embedding machine learning into a semi-physical empirical model (RF-PMRS). Specifically, based on the theory of the physical PM2.5 remote sensing (PMRS) approach, the complex parameter (VEf, a columnar volume-to-extinction ratio of fine particles) is simulated by the random forest (RF) model. Also, a fine-mode fraction product with higher quality is applied to make up for the insufficient coverage of satellite products. Experiments in North China (35∘–45∘N, 110∘–120∘E) show that the surface PM2.5 concentration derived by RF-PMRS has an average annual value of 57.92 µg m−3 vs. the ground value of 60.23 µg m−3. Compared with the original method, RMSE decreases by 39.95 µg m−3, and the relative deviation is reduced by 44.87 %. Moreover, validation at two Aerosol Robotic Network (AERONET) sites presents a time series change closer to the true values, with an R of about 0.80. This study is also a preliminary attempt to combine model-driven and data-driven models, laying the foundation for further atmospheric research on optimization methods.

An IoT System and MODIS Images Enable Smart Environmental Management for Mekong Delta

The smart environmental management system proposed in this work offers a new approach to environmental monitoring by utilizing data from IoT stations and MODIS satellite imagery. The system is designed to be deployed in vast regions, such as the Mekong Delta, with low building and operating costs, making it a cost-effective solution for environmental monitoring. The system leverages telemetry data collected by IoT stations in combination with MODIS MOD09GA, MOD11A1, and MCD19A2 daily image products to develop computational models that calculate the values land surface temperature (LST), 2.5 and 10 (µm) particulate matter mass concentrations (PM2.5 and PM10) in areas without IoT stations. The MOD09GA product provides land surface spectral reflectance from visible to shortwave infrared wavelengths to determine land cover types. The MOD11A1 product provides thermal infrared emission from the land surface to compute LST. The MCD19A2 product provides aerosol optical depth values to detect the presence of atmospheric aerosols, e.g., PM2.5 and PM10. The collected data, including remote sensing images and telemetry sensor data, are preprocessed to eliminate redundancy and stored in cloud storage services for further processing. This allows for automatic retrieval and computation of the data by the smart data processing engine, which is designed to process various data types including images and videos from cameras and drones. The calculated values are then made available through a graphic user interface (GUI) that can be accessed through both desktop and mobile devices. The GUI provides real-time visualization of the monitoring values, as well as alerts to administrators based on predetermined rules and values of the data. This allows administrators to easily monitor the system, configure the system by setting alerting rules or calibrating the ground stations, and take appropriate action in response to alerts. Experimental results from the implementation of the system in Dong Thap Province in the Mekong Delta show that the linear regression models for PM2.5 and PM10 estimations from MCD19A2 AOD values have correlation coefficients of 0.81 and 0.68, and RMSEs of 4.11 and 5.74 µg/m3, respectively. Computed LST values from MOD09GA and MOD11A1 reflectance and emission data have a correlation coefficient of 0.82 with ground measurements of air temperature. These errors are comparable to other models reported in similar regions in the literature, demonstrating the effectiveness and accuracy of the proposed system.