Air Quality Atmosphere and Health最新文献

Exposure to the high volatile organic compounds (VOCs) concentration in the workplaces where solvents are used is an essential point for worker’s health. However, the VOCs in the indoor air of an adhesive tape production facilities that use large amounts of solvents and the health risk of the toxic compounds have not been sufficiently investigated to this date. VOC samples were collected in the morning and afternoon times of day in the indoor air of workplaces of an adhesive tape production facility at 9 different points under two different central ventilation conditions. Carbon dioxide, humidity, temperature, flow rate, and pressure values were measured continuously throughout the work time with an average recording period of 1 min. The total VOC value had a wide range from 0.1 to 138 mg/m³. BTEX (benzene, toluene, ethylbenzene, xylenes) contribution to total VOC accounted for between 40 and 60% and the toluene, methylepentane, and trichloroethane concentrations among the sampling points and campaigns dominated the total VOCs. The total hazard quotient (HQ) values for each measurement campaigns were higher than the acceptable limit of 1.0, while the lifetime cancer risk (LCR) values for benzene and carbon tetrachloride were lower than the acceptable limit of 1.0×10⁻⁶. This observational study suggests that the effective and efficient operation of the workplace ventilation systems and the feasibility of the designed ventilation systems are essential on the accumulation of toxic compounds in the air and must be well evaluated.

In the glass industrial city Firozabad, real-time monitoring (mass as well as number) of size-segregated particulate matter (PM) was done by a GRIMM portable aerosol spectrometer at two different sites to know about the deposition of size-segregated PM in the human respiratory tract. The average mass concentrations of PMs were recorded as PM₁₀ (184.68 μg/m⁻³), PM_2.5 (54.48 μg/m⁻³), and PM_1.0 (31.02 μg/m⁻³). PM number concentrations were found as PM₁₀ (18.64 μg/m⁻³), PM_2.5 (496.22 μg/m⁻³), and PM_1.0 (1105.87 μg/m⁻³). The concentrations of PM₁₀ and PM_2.5 exceeded the National Ambient Air Quality Standards (NAAQS) and the World Health Organization (WHO) guidelines. It was observed that PM₁₀ and PM_2.5 were the highest deposited in the head region (99.58–84.66%, 92.02–32.70%, 99.56–85.05%, and 97.20–69.25%) followed by the tracheobronchial (TB) region respectively in urban and rural. It was revealed that children with 3 and 9 years age group have the highest deposition and highly affected by lung diseases in both sampling sites. The children in the urban site have highly deposited PM mass visualization as compared to the rural site. Hazard quotient (HQ) results showed that a sensitive exposed population (children) may be at non-carcinogenic risk from acute exposure to PM₁₀ in urban (3.83) as well as in rural site (2.971) because the safer limit (HQ > 1) the prescribed by USEPA is exceeded, while the excess lifetime cancer risk (ELCR) assessment of PM_2.5 for both adult (68.7 × 10⁻², 45.8 × 10⁻²) and child (195.4 × 10⁻¹, 130.2 × 10⁻¹) exceeded the safer limit (≥ 10⁻⁶) in both sites which inferred greater carcinogenic risk for adults and children.

The deployment of air quality monitoring stations is limited in number and unevenly distributed, resulting in a limited number of collected samples, so fine-grained analysis of air quality is a challenging task. To solve the problem, a spatio-temporal fusion graph convolution network method based on an attention mechanism named ASTGC is proposed in this paper. The ASTGC divides the observation data and various influencing factors of monitoring stations and target locations into dynamic temporal features and static spatial features. Different models are used to model them separately, and then fuse them to achieve the purpose of spatio-temporal interaction. An attention mechanism is introduced to calculate the importance of different monitoring stations to construct an adjacency matrix for the target location. Finally, the AQI of the target location in the future is predicted through a graph convolution network. The proposed model is evaluated on a real dataset, and the experimental results demonstrate the method’s superiority over baseline models.

Bustling port activities give rise to serious air pollution in port cities of China’s Yangtze River Delta (YRD). Understanding the characteristics and driving forces of air pollutants over port cities of the YRD is conducive to the prevention and control of air pollution. This study analyzed the spatiotemporal characteristics and influencing factors of three air pollutants—PM_2.5, NO₂, and SO₂—over coastal port cities (CPCs) and inland port cities (IPCs) in YRD from 2015 to 2020. The concentrations of air pollutants vary across geographic locations (low in the south/east and high in the north/west) and seasons (low in summer and high in winter). IPCs show higher pollutant concentrations than CPCs. The PM_2.5, NO₂, and SO₂ concentrations over port cities in the YRD declined by 41.48%, 18.68%, and 64.8% from 2015 to 2020; CPCs have reduced more PM_2.5 and NO₂ than IPCs, while IPCs have reduced more SO₂ than CPCs. There is a high synergy among the three air pollutants, with a stronger synergy found in CPCs. The impacts of wind speed and boundary layer height on air pollution are greater in CPCs than in IPCs. Concentrations of different air pollutants are strongly associated with emissions from different sectors. NO₂ is the only one among the three air pollutants that cargo throughput shows a significant impact on, with the impact greater in CPCs than in IPCs. Findings from this study deepen the understanding of air pollution in port cities of YRD and may support air quality control in this area.

Commuters’ exposure to size-segregated fine particulates in four public transport microenvironments was assessed in the Kolkata megacity of India. Personal exposure to PM_2.5 and PM₁ varied from 130.8 & 112.1 μg m⁻³ in air-conditioned (AC) buses, followed by 158.5 μg m⁻³ & 134.3 μg m⁻³ in non-AC buses, 187.1 μg m⁻³ & 150.8 μg m⁻³ in non-AC cars, to 242.2 μg m⁻³ & 199.6 μg m⁻³ in 3-wheeler auto rickshaws, respectively. The exposure ratio for PM₁/PM_2.5 was comparable in all transport modes (0.64 to 0.94, 0.83 ± 0.07). The micromorphology of fine particulates, studied by scanning electron microscopy-energy-dispersive X-ray spectrometry, revealed several morphological features in both inorganic and carbonaceous particulates with Al, Si, Ca, K, Fe, and S impregnations. Soot particles were predominantly present in PM_< 0.25, and its semi-aggregated net-like structure trapped fine and ultrafine particles. The possible formation of carbonaceous aerosols from inorganic seeds via the nucleation pathway was also captured. The estimated deposition rate in the human respiratory system translated into a total PM_2.5 respiratory deposition rate (RDR) of 25.5 ± 8.9 μg h⁻¹ in the respiratory tract was about 26% of the entire inhalation exposure to PM_2.5. The average RDR of PM_2.5–1.0 and PM_1.0–0.5 was 11.7 ± 5.9 μg h⁻¹ and 4.5 ± 2.3 μg h⁻¹ that may get preferentially deposited in the head airways of the human respiratory system (75% and 60%, respectively). While the finest particles mainly get deposited in the deepest alveolar region of the human respiratory system (the RDR for PM_0.5–0.25 and PM_< 0.25 was 3.5 ± 1.5 μg h⁻¹ (49%) and 5.8 ± 2.5 μg h⁻¹ (79%), respectively). The highest airway deposition of PM_2.5 in auto rickshaw commuters indicates that this transport mode could be the most harmful to commuters exposing them to tailpipe emissions from on-road vehicles and resuspended road dust due to low floor height and the open nature of the vehicle. Auto rickshaw commuters should practice using PM_2.5-restricting face masks to reduce exposure to fine particulates while commuting when this mode of commute cannot be avoided.

Tourism development makes significant contributions to the modern economic system, while simultaneously generating profound environmental externalities. However, the effects and mechanisms by which tourism development affects air pollution are still unclear, possibly due to model uncertainty arising from the complexity of air pollution sources. Therefore, based on a novel combined empirical strategy and high-quality multi-source datasets of 284 Chinese cities, this paper uncovers whether and how tourism development can affect air pollution in the context of “government, enterprise, and public” pluralistic environmental co-governance. The results reveal that: (1) Tourism development has a significant air pollution mitigation effect in China’s campaign against air pollution. A 1% increase in tourism specialization directly reduces air pollution by 0.1925%. (2) Two single mediation channels are found: “tourism development → public environmental concern → air pollution”, “tourism development → industrial structure → air pollution”. Additionally, there are two serial mediation channels: “tourism development → government environmental governance → public environmental concern → air pollution” and “tourism development → government environmental governance → industrial structure → air pollution”. (3) The above evidence provides meaningful policy implications for China to address the intractable problem of environmental governance. (4) The empirical strategy of innovatively integrating Bayesian model averaging, structural equation modeling, and panel data is effective, especially for macroscopic variables with complex influence mechanisms. (5) The relevant empirical evidence may have limited external validity outside of China, but the proposed empirical strategy can provide new strategic options to reveal the multi-path mechanisms at the macro level.

There is increasing use of satellite data for environmental monitoring in data-sparse regions of the world. However, challenges such as cloud cover and swath area of satellite sensors necessitate the validation of satellite retrievals, particularly in regions where ground-based measurements or high-density coverage may be sparse. In this study, the performance of two NASA MODIS products—AQUA and TERRA—were compared with observational AERONET data over eight study sites in West Africa for the period 2000–2022. Results obtained showed a regression slope between 0.09 and 0.83 for TERRA data and 0.11 and 0.86 for AQUA data. The normalized root mean square error between AERONET and AQUA data was in the range of 0.097–0.517, while a range of 0.123–0.540 was reported for TERRA data. Although both AQUA and TERRA AOD products had similar aerosol trends across the eight AERONET study sites, AQUA has an overall better performance with lower error estimates. Generally, the satellite retrieved data performed well during the wet season but poorly in the dry season. The performance of both MODIS products over the region suggests they can be used at most locations with little error/adjustment.

Radon emanation power was estimated by applying three different methods. The first was based on measuring radon exhalation rates by closed-loop accumulation method employing RAD7 device. Radon leakage rate was determined by applying two models of fitting the experimental data. Specific activities of ²²⁶Ra in soil samples were measured by coaxial HPGe detector (GEM30-70, ORTEC). The second method was indirect gamma-ray spectrometry method which included two separate measurements of counts under the photopeaks of 351.9 keV (²¹⁴Pb) and 609.3 keV (²¹⁴Bi). The influence of sample moisture content on radon emanation was demonstrated by both methods. Radon emanation power of the sample with the highest radon exhalation rate was also estimated by 2-month exposure of two radon diffusion chambers equipped with CR-39 detectors. A good agreement among the results was obtained; coefficient of variation was below 10% for the samples employed in the study. Assuming zero volumetric fraction of ²¹⁸Po in air provided more consistent results.

Excess concentration of aerosols with aerodynamic diameter less than 2.5 μm (PM_2.5) in atmosphere has significant implications to both climate and human health. For the first time, this study estimated four decades (1980–2021) trends of PM_2.5 in urban locations across Southern Africa using the Modern-Era Retrospective Analysis for Research and Applications Version 2 (MERRA-2) aerosol simulations. The findings show that the highest PM_2.5 is recorded during the peak of biomass burning, September-October-November (SON season) in locations selected over Mozambique, Malawi, Zambia, Zimbabwe, Madagascar, Botswana, Namibia, and north of South Africa while during Jun-July-August (JJA season) in locations selected over Southern Mozambique, eastern locations of South Africa, and west of Angola. The lowest PM_2.5 concentration is observed during December-January-February (DJF) and March-April-May (MAM) seasons across the region due to increased precipitation which reduces excess PM_2.5 in the atmosphere. The annual concentration of PM_2.5 in most locations exceeds the recent recommendation by the World Health Organization (WHO, 2021) of 5μg m⁻³, hence high threat to human health due to long-term exposure to PM_2.5. Long-term trends showed a significant increase of PM_2.5 over the region during the last four decades, with the highest increment observed during SON season. Implementation of regional measures that can reduce excess PM_2.5 concentration is therefore required across the region.

The concentration of air pollutants is closely related to people’s production and life. Air quality prediction is the premise for environmental management departments to make decisions and put forward pollution control measures. A novel air pollutant prediction model was proposed in this paper to predict air pollutant concentration more accurately. Firstly, the data were decomposed into several subsequences by a complete ensemble empirical mode decomposition with adaptive noise and calculated the sample entropy of the subsequence. Secondly, variational mode decomposition is used to decompose the sequence with the highest sample entropy, and a fast correlation-based filter is used to select the features of the second decomposed sequence and the remaining sequences. Then, a multi-layer perceptron is used to predict the processed quadratic decomposition sequence, and a gated recurrent unit is used to predict the remaining sequences. According to the experimental results, three main conclusions can be drawn. First, through two groups of comparative experiments, it is found that the model has a good prediction effect. Second, after adding the decomposition algorithm, the average improvement levels of mean absolute error and root mean squared error were 44.50% and 34.77%, respectively. Third, after the re-decomposition of intrinsic mode functions 1, the mean absolute percentage error can be reduced by 22.98% on average on the original basis. The results of this study can provide a valuable reference for the prediction of atmospheric pollutants.