Seasonal variation in health impacts associated with visibility in Beijing, China

Evaluating the mortality and health rate caused by the PM2.5 pollutant in the air of several important Iranian cities and evaluating the effect of variables with a linear time series model

All over the world, the level of special air pollutants that have the potential to cause diseases is increasing. Although the relationship between exposure to air pollutants and mortality has been proven, the health risk assessment and prediction of these pollutants have a therapeutic role in protecting public health, and need more research. The purpose of this research is to evaluate the ill-health caused by PM2.5 pollution using AirQ + software and to evaluate the different effects on PM2.5 with time series linear modeling by R software version 4.1.3 in the cities of Arak, Esfahan, Ahvaz, Tabriz, Shiraz, Karaj and Mashhad during 2019-2020. The pollutant hours, meteorology, population and mortality information were calculated by the Environmental Protection Organization, Meteorological Organization, Statistics Organization and Statistics and Information Technology Center of the Ministry of Health, Treatment and Medical Education for 24 h of PM2.5 pollution with Excel software. In addition, having 24 h of PM2.5 pollutants and meteorology is used to the effect of variables on PM2.5 concentration. The results showed that the highest and lowest number of deaths due to natural deaths, ischemic heart disease (IHD), lung cancer (LC), chronic obstructive pulmonary disease (COPD), acute lower respiratory infection (ALRI) and stroke in The effect of disease with PM2.5 pollutant in Ahvaz and Arak cities was 7.39-12.32%, 14.6-17.29%, 16.48-8.39%, 10.43-18.91%, 12.21-22.79% and 14.6-18.54 % respectively. Another result of this research was the high mortality of the disease compared to the mortality of the nose. The analysis of the results showed that by reducing the pollutants in the cities of Karaj and Shiraz, there is a significant reduction in mortality and linear modeling provides a suitable method for air management planning.

Haze weather and mortality in China from 2014 to 2020: Definitions, vulnerability, and effect modification by haze characteristics

Haze weather, characterized by low visibility due to severe air pollution, has aroused great public concern. However, haze definitions are inconclusive, and multicentre studies on the health impacts of haze are scarce. We collected data on the daily number of deaths and environmental factors in 190 Chinese cities from 2014 to 2020. The city-specific association was estimated using quasi-Poisson regression and then pooled using meta-analysis. We found a negative association between daily visibility and non-accidental deaths, and mortality risk sharply increased when visibility was < 10 km. Haze weather, defined as a daily average visibility of < 10 km without a limit for humidity, produced the best model fitness and greatest effect on mortality. A haze day was associated with an increase of 2.53% (95% confidence interval [CI]:1.96, 3.10), 2.84 (95% CI: 2.13, 3.56), and 2.99% (95% CI: 1.94, 4.04) in all non-accident, cardiovascular and respiratory mortality, respectively. Haze had the greatest effect on lung cancer mortality. The haze-associated risk of mortality increased with age. Severe haze (visibility <2 km) and damp haze (haze with relative humidity >90%) had greater health impacts. Our findings can help in the development of early warning systems and effective public health interventions for haze.

A Multi-Modal Deep-Learning Air Quality Prediction Method Based on Multi-Station Time-Series Data and Remote-Sensing Images: Case Study of Beijing and Tianjin

The profound impacts of severe air pollution on human health, ecological balance, and economic stability are undeniable. Precise air quality forecasting stands as a crucial necessity, enabling governmental bodies and vulnerable communities to proactively take essential measures to reduce exposure to detrimental pollutants. Previous research has primarily focused on predicting air quality using only time-series data. However, the importance of remote-sensing image data has received limited attention. This paper proposes a new multi-modal deep-learning model, Res-GCN, which integrates high spatial resolution remote-sensing images and time-series air quality data from multiple stations to forecast future air quality. Res-GCN employs two deep-learning networks, one utilizing the residual network to extract hidden visual information from remote-sensing images, and another using a dynamic spatio-temporal graph convolution network to capture spatio-temporal information from time-series data. By extracting features from two different modalities, improved predictive performance can be achieved. To demonstrate the effectiveness of the proposed model, experiments were conducted on two real-world datasets. The results show that the Res-GCN model effectively extracts multi-modal features, significantly enhancing the accuracy of multi-step predictions. Compared to the best-performing baseline model, the multi-step prediction's mean absolute error, root mean square error, and mean absolute percentage error increased by approximately 6%, 7%, and 7%, respectively.

Prediction and analysis of atmospheric visibility in five terrain types with artificial intelligence

Scattering visiometers are widely used to measure atmospheric visibility; however, visibility is difficult to measure accurately because the extinction coefficient decays exponentially with visual range according to the Koschmid's law. Moreover, models for predicting visibility are lacking due to the lack of accurate visibility observations to verify. This study formulated an artificial intelligence method for measuring atmospheric visibility in five topographical regions: hills, basins, plains, alluvial plains, and rift valleys. Four air pollution factors and five meteorological factors were selected as independent variables for predicting visibility by using three artificial intelligence models, namely a support vector machine (SVM) model, a multilayer perceptron (MLP) model, and an extreme gradient boosting (XGBoost) model. The GridSearchCV function was used to automatically tune model hyperparameters to determine the optimal parameter values of the three models for the five target areas. The predictions of the aforementioned three models underwent considerable considerably scale shrinking relative to observed values. The inappropriately low predicted visibility values might have been caused by the use of inaccurate observations for training. To solve this problem, formulas of scale ratio and downshift were used to adjust the predicted values. Statistical measurements of model performance measures by five quantitative methods (e.g., correlation coefficient, mean absolute error) showed that adjusted predictions were in strong agreement with the observation data for the five target areas. Therefore, the adjusted prediction has high reliability. Because of obvious differences in the topography, weather, and air quality of the five target areas, different models provided optimal predictions for different areas. In densely populated western Taiwan, the MLP model is most suitable for predicting visibility on hills whereas the XGBoost model is most suitable for predicting visibility on basins and plains. In eastern Taiwan, the SVM model is most suitable for predicting visibility on alluvial plains and rift valleys. Thus, the optimal prediction model should be identified according to the conditions in each area. These results can inform decision-making processes or improve visibility predicting in specific areas.

Effects of pollen concentration on allergic rhinitis in children: A retrospective study from Beijing, a Chinese megacity

With global climate change and rapid urbanization, the prevalence of allergic diseases caused by pollen is rising dramatically worldwide with unprecedented complexity and severity, especially for children in mega-cities. However, because of the lack of long time-series pollen concentrations data, the accurate evaluation of the impact of pollen on allergic rhinitis (AR) was scarce in the Chinese metropolis. A generalized additive model was used to assess the effect of pollen concentration on pediatric AR outpatient visits in Beijing from 2014 to 2019. A stratified analysis of 10 pollen species and age-gender-specific groups was also conducted during the spring and summer-autumn peak pollen periods separately. Positive associations between pollen concentration and pediatric AR varied with the season and pollen species were detected. Although the average daily pollen concentration is higher during the spring tree pollen peak, the influence was stronger at the summer-autumn weed pollen peak with the maximum relative risk 1.010 (95% CI 1.009, 1.011), which was higher than the greatest relative risk, 1.003 (95% CI 1.002, 1.004) in the spring peak. The significant adverse effects can be sustained to lag10 during the study period, and longer in the summer-autumn peak (lag13) than in the spring peak (lag8). There are thresholds for the health effects and they varied between seasons. The significant effect appeared when the pollen concentration was higher than 3.74 × 105 grain·m-2·d-1 during the spring tree pollen peaks and 4.70 × 104 grain·m-2·d-1 during the summer-autumn weed pollen peaks. The stratified results suggested that the species-specific effects were heterogeneous. It further highlights that enough attention should be paid to the problem of pollen allergy in children, especially school-aged children aged 7-18 years and weed pollen in the summer-autumn peak pollen period. These findings provide a more accurate reference for the rational coordination of medical resources and improvement of public health.

Chemical composition, oxidative potential and identifying the sources of outdoor PM2.5 after the improvement of air quality in Beijing

Air pollution poses a serious threat to human health. The implementation of air pollution prevention and control policies has gradually reduced the level of atmospheric fine particles in Beijing. Exploring the latest characteristics of PM_2.5 has become the key to further improving pollution reduction measures. In the current study, outdoor PM_2.5 samples were collected in the spring and summer of Beijing, and the chemical species, oxidative potential (OP), and sources of PM_2.5 were characterized. The mean PM_2.5 concentration during the entire study period was 41.6 ± 30.9 μg m^-3. Although the PM_2.5 level in summer was lower, its OP level was significantly higher than that in spring. SO₄^2-, NH₄⁺, EC, NO^3-, and OC correlated well with volume-normalized OP (OP_v). Strong positive correlations were found between OP_v and the following elements: Cu, Pb, Zn, Ni, As, Cr, Sn, Cd, Al, and Mn. Seven sources of PM_2.5 were identified, including traffic, soil dust, secondary sulfate, coal and biomass burning, oil combustion, secondary nitrate, and industry. Multiple regression analysis indicated that coal and biomass combustion, industry, and traffic were the main contributors to the OP_v in spring, while secondary sulfate, oil combustion, and industry played a leading role in summer. The source region analysis revealed that different pollution sources were related to specific geographic distributions. In addition to local emission reduction policies, multi-provincial cooperation is necessary to further improve Beijing's air quality and reduce the adverse health effects of PM_2.5.

Potential influence of fine aerosol chemistry on the optical properties in a semi-arid region

The current understanding regarding the potential influence of aerosol chemistry on the optical properties does not satisfy accurate evaluation of aerosol radiative effects and precise determination of aerosol sources. We conducted a comprehensive study of the potential influence of aerosol chemistry on the optical properties in a semi-arid region based on various observations. Organic matter was the main contributor to the scattering coefficients followed by secondary inorganic aerosols in all seasons. We further related aerosol absorption to elemental carbon, organic matter, and mineral dust. Results showed that organic matter and mineral dust contributed to >40% of the aerosol absorption in the ultraviolet wavelengths. Therefore, it is necessary to consider the absorption of organic matter and mineral dust in addition to that of elemental carbon. We further investigated the potential influence of chemical composition, especially of organic matter and mineral dust on the optical parameters. Mineral dust contributed to higher absorption efficiency and lower scattering efficiency in winter. The absorption Ångström exponent (AAE) was mostly sensitive to organic matter and mineral dust in winter and spring, respectively; it was relatively high (i.e., 1.68) in winter and moderate (i.e., 1.42) in spring. Unlike in the other seasons, mineral dust contributed to higher mass absorption efficiency in winter. This work reveals the complexity of the relationship between aerosol chemistry and optical properties, and especially the influence of organic matter and mineral dust on aerosol absorption. The results are highly important regarding both regional air pollution and climate.

The influences of the East Asian Monsoon on the spatio-temporal pattern of seasonal influenza activity in China

Previous research has extensively studied the seasonalities of human influenza infections and the effect of specific climatic factors in different regions. However, there is limited understanding of the influences of monsoons. This study applied generalized additive model with monthly surveillance data from mainland China to explore the influences of the East Asian Monsoon on the spatio-temporal pattern of seasonal influenza in China. The results suggested two influenza active periods in northern China and three active periods in southern China. The study found that the northerly advancement of East Asian Summer Monsoon (EASM) influences the summer influenza spatio-temporal patterns in both southern and northern China. At the interannual scale, the north-south converse effect of EASM on influenza activity is mainly due to the converse effect of EASM on humidity and precipitation. Within the annual scale, influenza activity in southern China gradually reaches its maximum during the summer exacerbated by the northerly advancement of EASM. Furthermore, the winter epidemic in China is related to the low temperature and humidity influenced by the East Asian Winter Monsoon (EAWM). Moreover, the active period in transition season is related partially to the large rapid temperature change influenced by the transition of EAWM and EASM. Despite the delayed onset and instability, the climatic condition influenced by the East Asian Monsoon is one of the potential key drivers of influenza activity.

Oxidative potential and water-soluble heavy metals of size-segregated airborne particles in haze and non-haze episodes: Impact of the "Comprehensive Action Plan" in China

Air pollution is a major environmental health challenge in megacities, and as such a Comprehensive Action Plan (CAP) was issued in 2017 for Beijing, the capital city of China. Here we investigated the size-segregated airborne particles collected after the implementation of the CAP, intending to understand the change of oxidative potential and water-soluble heavy metal (WSHM) levels in 'haze' and 'non-haze' days. The DNA damage and the levels of WSHM were analyzed by Plasmid Scission Assay (PSA) and High-Resolution Inductively Coupled Plasma Mass Spectrometry (HR-ICP-MS) techniques. The PM mass concentration was higher in the fine particle size (0.43-2.1 μm) during haze days, except for the samples affected by mineral dust. The particle-induced DNA damage caused by fine sized particles (0.43-2.1 μm) exceeded that caused by the coarse sized particles (4.7-10 μm). The DNA damage from haze day particles significantly exceeded those collected on non-haze days. Prior to the instigation of the CAP, the highest value of DNA damage decreased, and DNA damage was seen in the finer size (0.43-1.1 μm). The Pearson correlation coefficient between the concentrations of water-soluble Pb, Cr, Cd and Zn were positively correlated with DNA damage, suggesting that these WSHM had significant oxidative potential. The mass concentrations of water-soluble trace elements (WSTE) and individual heavy metals were enriched in the finer particles between 0.43 μm to 1.1 μm, implying that smaller sized particles posed higher health risks. In contrast, the significant reduction in the mass concentration of water-soluble Cd and Zn, and the decrease of the maximum and average values of DNA damage after the CAP, demonstrated its effectiveness in restricting coal-burning emissions. These results have demonstrated that the Beijing CAP policy has been successful in reducing the toxicity of 'respirable' ambient particles.

Exploring the Sensitivity of Visibility to PM2.5 Mass Concentration and Relative Humidity for Different Aerosol Types

Fine particle (PM2.5) mass concentration and relative humidity (RH) are the primary factors influencing atmospheric visibility. There are some studies focused on the complex, nonlinear relationships among visibility, PM2.5 concentration, and RH. However, the relative contribution of the two factors to visibility degradation, especially for different aerosol types, is difficult to quantify. In this study, the normalized forward sensitivity index method for identifying the dominant factors of visibility was used on the basis of the sensitivity of visibility to PM2.5 and RH changes. The visibility variation per unit of PM2.5 or RH was parameterized by derivation of the visibility multivariate function. The method was verified and evaluated based on 4453 valid hour data records in Tianjin, and visibility was identified as being in the RH-sensitive regime when RH was above 75%. In addition, the influence of aerosol chemical compositions on sensitivity of visibility to PM2.5 and RH changes was discussed by analyzing the characteristics of extinction components ((NH4)2SO4, NH4NO3, organic matter, and elemental carbon) measured in Tianjin, 2015. The result showed that the fitting equation of visibility, PM2.5, and RH, separately for different aerosol types, further improved the accuracy of the parameterization scheme for visibility in most cases.

Relationship between Visibility, Air Pollution Index and Annual Mortality Rate in Association with the Occurrence of Rainfall—A Probabilistic Approach

An innovative method was proposed to facilitate the analyses of meteorological conditions and selected air pollution indices’ influence on visibility, air quality index and mortality. The constructed calculation algorithm is dedicated to simulating the visibility in a single episode, first of all. It was derived after applying logistic regression methodology. It should be stressed that eight visibility thresholds (Vis) were adopted in order to build proper classification models with a number of relevant advantages. At first, there exists the possibility to analyze the impact of independent variables on visibility with the consideration of its’ real variability. Secondly, through the application of the Monte Carlo method and the assumed classification algorithms, it was made possible to model the number of days during a precipitation and no-precipitation periods in a yearly cycle, on which the visibility ranged practically: Vis < 8; Vis = 8–12 km, Vis = 12–16 km, Vis = 16–20 km, Vis = 20–24 km, Vis = 24–28 km, Vis = 28–32 km, Vis > 32 km. The derived algorithm proved a particular role of precipitation and no-precipitation periods in shaping the air visibility phenomena. Higher visibility values and a lower number of days with increased visibility were found for the precipitation period contrary to no-precipitation one. The air quality index was lower for precipitation days, and moreover, strong, non-linear relationships were found between mortality and visibility, considering precipitation and seasonality effects.

Influence of the seasonality and of urban variables in the BTEX and PM2.5 atmospheric levels and risks to human health in a tropical coastal city (Fortaleza, CE, Brazil)

The International Agency for Research on Cancer (IARC) classifies benzene in group 1 (carcinogenic to humans). Particulate matter (PM) has recently also been classified in this category. This was an advance toward prioritizing the monitoring of particles in urban areas. The aim of the present study was to assess levels of PM_2.5 and BTEX (benzene, toluene, ethylbenzene, and xylene), the influence of meteorological variables, the planetary boundary layer (PBL), and urban variables as well as risks to human health in the city of Fortaleza, Brazil, in the wet and dry periods. BTEX compounds were sampled using the 1501 method of NIOSH and determined by GC-HS-PID/FID. PM_2.5 was monitored using an air sampling pump with a filter holder and determined by the gravimetric method. Average concentrations of BTEX ranged from 1.6 to 45.5 μg m^-3, with higher values in the wet period, which may be explained by the fact that annual distribution is influenced by meteorological variables and the PBL. PM_2.5 levels ranged from 4.12 to 33.0 μg m^-3 and 4.18 to 86.58 μg m^-3 in the dry and wet periods, respectively. No seasonal pattern was found for PM_2.5, probably due to the influence of meteorological variables, the PBL, and urban variables. Cancer risk ranged from 2.46E^-04 to 4.71E^-03 and 1.72E^-04 to 2.01E^-03 for benzene and from 3.07E^-06 to 7.04E^-05 and 3.08E^-06 to 2.85E^-05 for PM_2.5 in the wet and dry periods, respectively. Cancer risk values for benzene were above the acceptable limit established by the international regulatory agency in both the dry and wet periods. The results obtained of the noncarcinogenic risks for the compounds toluene, ethylbenzene, and xylene were within the limits of acceptability. The findings also showed that the risk related to PM is always greater among smokers than nonsmokers.

Visibility Driven Perception and Regulation of Air Pollution in Hong Kong, 1968–2020

Visibility is a perceptible indicator of air pollution, so it is hardly surprising that it has been used to promote the regulation of air pollutants. In Hong Kong, poor visibility associated with air pollution has been linked with changes in tourist choices and health outcomes. Much research is available to examine the early deterioration of visibility in the city, and especially its relation to particulate sulfate. The period 2004–2012 saw especially poor visibility in Hong Kong and coincided with a time when pollutant levels were high. There is a reasonable correlation (multiple r2 = 0.57) between the monthly hours of low visibility (<8 km) and PM10, NO2, SO2, and O3 concentrations from the late 1990s. Visibility can thus be justified as a route to perceiving air pollution. Over the last decade, visibility has improved and average pollutant concentrations have declined in Hong Kong. The changing health risk from individual pollutants parallels their concentration trends: the risk from NO2 and particulate matter at urban sites has declined, but there have been increases in the health risks from ozone as its concentrations have risen across the region, although this is dominated by concentration increases at more rural sites. Since 2004, the frequency of search terms such as visibility, air pollution, and haze on Google has decreased in line with improved visibility. Despite positive changes to Hong Kong’s air quality, typically, the media representation and public perception see the situation as growing more severe, possibly because attention focuses on the air quality objectives in Hong Kong being less stringent than World Health Organisation guidelines. Policymakers increasingly need to account for the perceptions of stakeholders and acknowledge that these are not necessarily bound to measurements from monitoring networks. Improvements in air quality are hard won, but conveying the nature of such improvements to the public can be an additional struggle.

The role of seasonality in the spread of COVID-19 pandemic

It has been reported that the transmission of COVID-19 can be influenced by the variation of environmental factors due to the seasonal cycle. However, its underlying mechanism in the current and onward transmission pattern remains unclear owing to the limited data and difficulties in separating the impacts of social distancing. Understanding the role of seasonality in the spread of the COVID-19 pandemic is imperative in formulating public health interventions. Here, the seasonal signals of the COVID-19 time series are extracted using the EEMD method, and a modified Susceptible, Exposed, Infectious, Recovered (SEIR) model incorporated with seasonal factors is introduced to quantify its impact on the current COVID-19 pandemic. Seasonal signals decomposed via the EEMD method indicate that infectivity and mortality of SARS-CoV-2 are both higher in colder climates. The quantitative simulation shows that the cold season in the Southern Hemisphere countries caused a 59.71 ± 8.72% increase of the total infections, while the warm season in the Northern Hemisphere countries contributed to a 46.38 ± 29.10% reduction. COVID-19 seasonality is more pronounced at higher latitudes, where larger seasonal amplitudes of environmental indicators are observed. Seasonality alone is not sufficient to curb the virus transmission to an extent that intervention measures are no longer needed, but health care capacity should be scaled up in preparation for new surges in COVID-19 cases in the upcoming cold season. Our study highlights the necessity of considering seasonal factors when formulating intervention strategies.