Characteristics of Particulate Pollution (PM2.5 and PM10) and Their Spacescale-Dependent Relationships with Meteorological Elements in China

Frequencies of micronuclei in buccal cells and their spatial distribution in a population living in proximity to coal mining areas in southern Brazil

The extraction and burning of coal release genotoxic pollutants, and understanding the relationship between genetic damage and the spatial distribution of residences in coal-using regions is crucial. The study aimed to conduct a spatial analysis of genotoxic damage through the of micronuclei (MNs) number and their proximity to coal mining/burning in the largest coal exploration region in Brazil. In this study, the detection of genotoxic damage was performed using the MN assay in oral cells of residents exposed to coal mining activities. Spatial analysis was conducted using QGIS 3.28.10 based on information obtained from a questionnaire administered to the population. Multiple linear regression analysis was carried out to assess the influence of the distance from residential areas to polluting sources on the number of MNs found. Additionally, Spearman's correlation was performed to identify the strength and direction of the association between the frequency of MNs and each of the polluting sources. A total of 147 MNs were quantified among all participants in the coal mining region. Notably, residents living within 2 km and 10 km of pollution sources exhibited the highest prevalence of MNs. The analysis demonstrated a significant correlation between closer proximity to pollution sources and increased MN frequency, underscoring the spatial relationship between these sources and genotoxic damage. Environmental pollutants from anthropogenic sources present a major health risk, potentially leading to irreversible damage. The spatial analysis in this study highlights the importance of targeted public policies. These policies should aim for a sustainable balance between economic development and public health, promoting effective measures to mitigate environmental impacts and protect community health.

Health effects of PM2.5 constituents and source contributions in major metropolitan cities, South Korea

Ambient PM_2.5 is one of the major risk factors for human health, and is not fully explained solely by mass concentration. We examined the short-term associations of cause-specific mortality (i.e., all-cause, cardiovascular, and respiratory mortality) with the 15 chemical constituents and sources of PM_2.5 in four metropolitan cities of South Korea during 2014-2018. We found transition metals consistently showed significant associations with all-cause mortality, while the effects of other constituents varied across the cities and for cause of death. Carbonaceous components strongly affected the all-cause, cardiovascular, and respiratory mortality in Daejeon. Secondary inorganic aerosols, SO₄^2- and NH₄⁺, showed significant associations with respiratory mortality in Gwangju. We also found the sources from which species closely linked to mortality generally increased the relative mortality risks. Heavy metal markers from soil or industrial sources were significantly associated with mortality in all cities. However, several sources influenced mortality despite their marker species not being significantly associated with it. Secondary nitrate and secondary sulfate sources were linked to mortality in DJ. This could be attributed to the deep inland location, which might have facilitated formation of secondary inorganic aerosols. In addition, primary sources including mobile and coal combustion seemed to have acute impacts on respiratory mortality in Gwangju. Our findings suggest the necessity of positive matrix factorization (PMF)-based approaches for evaluating health effects of PM_2.5 while considering the spatial heterogeneity in the compositions and source contributions of PM_2.5.

Potential hotspot modeling and monitoring of PM2.5 concentration for sustainable environmental health in Maharashtra, India

Modern human civilization has suffered from the disastrous impact of COVID-19, but it teaches us the lesson that the environment can restore its stability without human activity. The Government of India (GOI) has launched many strategies to prevent the situation of COVID-19, including a lockdown that has a great impact on the environment. The present study focuses on the analysis of Particulate Matter 2.5 (PM2.5) concentration levels in pre-locking, lockdown, and unlocking phases across ten major cities of Maharashtra (MH) that were the COVID hotspot of India during the COVID-19 outbreak; phase-wise and year-wise (2018-2020) hotspot analysis, box diagram and line graph methods were used to assess spatial variation in PM2.5 across MH cities. Our study showed that the PM2.5 concentration level was severe at pre-lockdown stage (January-March) and it decreased dramatically at the lockdown stage, later it also increased in its previous position at the unlocking stages, i.e., PM2.5 decreased dramatically (59%) during the lockdown period compared to the pre-lockdown period due to the shutdown of outdoor activities. It returns to its previous position due to the unlocking situation and increases (70%) compared to the lockdown period which illustrated the ups and downs of PM2.5 and ensures the position of different cities in the Air Quality Index (AQI) categories at different times. In the pre-lockdown phase, maximum PM2.5 concentration was in Navi Mumbai (NAV) (358) and Mumbai (MUM) (338), and Pune (PUN) (335) and Nashik NAS (325) subsequently, whereas at the last of the lockdown phase, it becomes Chandrapur (CHN) (82), Nagpur (NAG) (76), and Solapur (SOL) (45) subsequently. Hence, the restoration of the environment during the lockdown phase was temporary rather than permanent. Therefore, our findings propose that several effective policies of government such as relocation of polluting industries, short-term lockdown, odd-even vehicle number, installation of air purifier, and government strict initiatives are needed in making a sustainable environment.

A Suitable Model for Spatiotemporal Particulate Matter Concentration Prediction in Rural and Urban Landscapes, Thailand

Spatiotemporal particulate matter (PM) concentration prediction using MODIS AOD with significant PM factors in rural and urban landscapes in Thailand is necessary for public health and has been complicated by the limitations of PM monitoring stations. The research objectives were (1) to identify significant factors affecting PM10 concentrations in rural landscapes and PM2.5 in urban landscapes; (2) to predict spatiotemporal PM10 and PM2.5 concentrations using geographically weighted regression (GWR) and mixed-effect model (MEM), and (3) to evaluate a suitable spatiotemporal model for PM10 and PM2.5 concentration prediction and validation. The research methodology consisted of four stages: data collection and preparation, the identification of significant spatiotemporal factors affecting PM concentrations, the prediction of spatiotemporal PM concentrations, and a suitable spatiotemporal model for PM concentration prediction and validation. As a result, the predicted PM10 concentrations using the GWR model varied from 50.53 to 85.79 µg/m3 and from 36.92 to 51.32 µg/m3 in winter and summer, while the predicted PM10 concentrations using the MEM model varied from 50.68 to 84.59 µg/m3 and from 37.08 to 50.81 µg/m3 in both seasons. Likewise, the PM2.5 concentration prediction using the GWR model varied from 25.33 to 44.37 µg/m3 and from 16.69 to 24.04 µg/m3 in winter and summer, and the PM2.5 concentration prediction using the MEM model varied from 25.45 to 44.36 µg/m3 and from 16.68 and 23.75 µg/m3 during the two seasons. Meanwhile, according to Thailand and U.S. EPA standards, the monthly air quality index (AQI) classifications of the GWR and MEM were similar. Nevertheless, the derived average corrected Akaike Information Criterion (AICc) values of the GWR model for PM10 and PM2.5 predictions during both seasons were lower than that of the MEM model. Therefore, the GWR model was chosen as a suitable model for spatiotemporal PM10 and PM2.5 concentration predictions. Furthermore, the result of spatial correlation analysis for GWR model validation based on a new dataset provided average correlation coefficient values for PM10 and PM2.5 concentration predictions with a higher than the expected value of 0.5. Subsequently, the GWR model with significant monthly and seasonal factors could predict spatiotemporal PM 10 and PM2.5 concentrations in rural and urban landscapes in Thailand.

Temporal and Spatial Heterogeneity of PM2.5 Related to Meteorological and Socioeconomic Factors across China during 2000-2018

In recent years, air pollution caused by PM_2.5 in China has become increasingly severe. This study applied a Bayesian space-time hierarchy model to reveal the spatiotemporal heterogeneity of the PM_2.5 concentrations in China. In addition, the relationship between meteorological and socioeconomic factors and their interaction with PM_2.5 during 2000-2018 was investigated based on the GeoDetector model. Results suggested that the concentration of PM_2.5 across China first increased and then decreased between 2000 and 2018. Geographically, the North China Plain and the Yangtze River Delta were high PM_2.5 pollution areas, while Northeast and Southwest China are regarded as low-risk areas for PM_2.5 pollution. Meanwhile, in Northern and Southern China, the population density was the most important socioeconomic factor affecting PM_2.5 with q values of 0.62 and 0.66, respectively; the main meteorological factors affecting PM_2.5 were air temperature and vapor pressure, with q values of 0.64 and 0.68, respectively. These results are conducive to our in-depth understanding of the status of PM_2.5 pollution in China and provide an important reference for the future direction of PM_2.5 pollution control.

Particulate matter (PM10) prediction based on multiple linear regression: a case study in Chiang Rai Province, Thailand

Background

The northern regions of Thailand have been facing haze episodes and transboundary air pollution every year in which particulate matter, particularly PM₁₀, accumulates in the air, detrimentally affecting human health. Chiang Rai province is one of the country’s most popular tourist destinations as well as an important economic hub. This study aims to develop and compare the best-fitted model for PM₁₀ prediction for different seasons using meteorological factors.

Method

The air pollution and weather data acquired from the Pollution Control Department (PCD) spanned from the years 2011 until 2018 at two stations on an hourly basis. Four different stepwise Multiple Linear Regression (MLR) models for predicting the PM₁₀ concentration were then developed, namely annual, summer, rainy, and winter seasons.

Results

The maximum daily PM₁₀ concentration was observed in the summer season for both stations. The minimum daily concentration was detected in the rainy season. The seasonal variation of PM₁₀ was significantly different for both stations. CO was moderately related to PM₁₀ in the summer season. The PM₁₀ summer model was the best MLR model to predict PM₁₀ during haze episodes. In both stations, it revealed an R² of 0.73 and 0.61 in stations 65 and 71, respectively. Relative humidity and atmospheric pressure display negative relationships, although temperature is positively correlated with PM₁₀ concentrations in summer and rainy seasons. Whereas pressure plays a positive relationship with PM₁₀ in the winter season.

Conclusions

In conclusion, the MLR models are effective at estimating PM₁₀ concentrations at the local level for each seasonal. The annual MLR model at both stations indicates a good prediction with an R² of 0.61 and 0.52 for stations 65 and 73, respectively.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-021-12217-2.

Underestimated or overestimated? Dynamic assessment of hourly PM2.5 exposure in the metropolitan area based on heatmap and micro-air monitoring stations

Spatio-temporal distributions of air pollution and population are two important factors influencing the patterns of mortality and diseases. Past studies have quantified the adverse effects of long-term exposure to air pollution. However, the dynamic changes of air pollution levels and population mobility within a day are rarely taken into consideration, especially in metropolitan areas. In this study, we use the high-resolution PM_2.5 data from the micro-air monitoring stations, and hourly population mobility simulated by the heatmap based on Location Based Service (LBS) big data to evaluate the hourly active PM_2.5 exposure in a typical Chinese metropolis. The dynamic "active population exposure" is compared spatiotemporally with the static "census population exposure" based on census data. The results show that over 12 h on both study periods, 45.83% of suburbs' population-weighted exposure (PWE) is underestimated, while 100% of rural PWE and more than 34.78% of downtown's PWE are overestimated, with the relative difference reaching from -11 μg/m³ to 7 μg/m³. More notably, the total PWE of the active population at morning peak hours on weekdays is worse than previously realized, about 12.41% of people are exposed to PM_2.5 over 60 μg/m³, about twice as much as that in census scenario. The commuters who live in the suburbs and work in downtown may suffer more from PM_2.5 exposure and uneven environmental resource distribution. This study proposes a new approach of calculating population exposure which can also be extended to quantify other environmental issues and related health burdens.

Biological effect of PM10 on airway epithelium-focus on obstructive lung diseases

Recently, a continuous increase in environmental pollution has been observed. Despite wide-scale efforts to reduce air pollutant emissions, the problem is still relevant. Exposure to elevated levels of airborne particles increased the incidence of respiratory diseases. PM₁₀ constitute the largest fraction of air pollutants, containing particles with a diameter of less than 10 μm, metals, pollens, mineral dust and remnant material from anthropogenic activity. The natural airway defensive mechanisms against inhaled material, such as mucus layer, ciliary clearance and macrophage phagocytic activity, may be insufficient for proper respiratory function. The epithelium layer can be disrupted by ongoing oxidative stress and inflammatory processes induced by exposure to large amounts of inhaled particles as well as promote the development and exacerbation of obstructive lung diseases. This review draws attention to the current state of knowledge about the physical features of PM₁₀ and its impact on airway epithelial cells, and obstructive pulmonary diseases.

In-car particulate matter exposure across ten global cities

Cars are a commuting lifeline worldwide, despite contributing significantly to air pollution. This is the first global assessment on air pollution exposure in cars across ten cities: Dhaka (Bangladesh); Chennai (India); Guangzhou (China); Medellín (Colombia); São Paulo (Brazil); Cairo (Egypt); Sulaymaniyah (Iraq); Addis Ababa (Ethiopia); Blantyre (Malawi); and Dar-es-Salaam (Tanzania). Portable laser particle counters were used to develop a proxy of car-user exposure profiles and analyse the factors affecting particulate matter ≤2.5 μm (PM_2.5; fine fraction) and ≤10 μm (PM_2.5-10; coarse fraction). Measurements were carried out during morning, off- and evening-peak hours under windows-open and windows-closed (fan-on and recirculation) conditions on predefined routes. For all cities, PM_2.5 and PM₁₀ concentrations were highest during windows-open, followed by fan-on and recirculation. Compared with recirculation, PM_2.5 and PM₁₀ were higher by up to 589% (Blantyre) and 1020% (São Paulo), during windows-open and higher by up to 385% (São Paulo) and 390% (São Paulo) during fan-on, respectively. Coarse particles dominated the PM fraction during windows-open while fine particles dominated during fan-on and recirculation, indicating filter effectiveness in removing coarse particles and a need for filters that limit the ingress of fine particles. Spatial variation analysis during windows-open showed that pollution hotspots make up to a third of the total route-length. PM_2.5 exposure for windows-open during off-peak hours was 91% and 40% less than morning and evening peak hours, respectively. Across cities, determinants of relatively high personal exposure doses included lower car speeds, temporally longer journeys, and higher in-car concentrations. It was also concluded that car-users in the least affluent cities experienced disproportionately higher in-car PM_2.5 exposures. Cities were classified into three groups according to low, intermediate and high levels of PM exposure to car commuters, allowing to draw similarities and highlight best practices.

Spatiotemporal patterns of PM2.5 elemental composition over China and associated health risks

Trace metals in atmospheric particulate matter (PM) are a serious threat to public health. Although pollution from toxic metals has been investigated in many Chinese cities, the spatial and temporal patterns in PM_2.5 remain largely unknown. Long-term PM_2.5 field sampling in 11 cities, combined with a systemic literature survey covering 51 cities, provides the first comprehensive database of 21 PM_2.5-bound trace metals in China. Our results revealed that PM_2.5 elemental compositions varied greatly, with generally higher levels in North China, especially for crustal elements. Pollution with Cr, As, and Cd was most serious, with 61, 38, and 16 sites, respectively, surpassing national standards, including some in rural areas. Local emissions, particularly from metallurgical industries, were the dominant factors driving the distribution in polluted cities such as Hengyang, Yuncheng, and Baiyin, which are mainly in North and Central China. Elevated As, Cd, and Cr levels in Yunnan, Guizhou Province within Southwest China were attributed to the high metal content of local coal. Diverse temporal trends of various elements that differed among regions indicated the complexity of emission patterns across the country. The results demonstrated high non-carcinogenic risks for those exposed to trace metals, especially for children and residents of heavily cities highly polluted with As, Pb, or Mn. The estimated carcinogenic risks ranged from 6.61 × 10^-6 to 1.92 × 10^-4 throughout China, with As being the highest priority element for control, followed by Cr and Cd. Regional diversity in major toxic metals was also revealed, highlighting the need for regional mitigation policies to protect vulnerable populations.

Influence of meteorological conditions on PM2.5 concentrations across China: A review of methodology and mechanism

Air pollution over China has attracted wide interest from public and academic community. PM_2.5 is the primary air pollutant across China. Quantifying interactions between meteorological conditions and PM_2.5 concentrations are essential to understand the variability of PM_2.5 and seek methods to control PM_2.5. Since 2013, the measurement of PM_2.5 has been widely made at 1436 stations across the country and more than 300 papers focusing on PM_2.5-meteorology interactions have been published. This article is a comprehensive review on the meteorological impact on PM_2.5 concentrations_. We start with an introduction of general meteorological conditions and PM_2.5 concentrations across China, and then seasonal and spatial variations of meteorological influences on PM_2.5 concentrations. Next, major methods used to quantify meteorological influences on PM_2.5 concentrations are checked and compared. We find that causality analysis methods are more suitable for extracting the influence of individual meteorological factors whilst statistical models are good at quantifying the overall effect of multiple meteorological factors on PM_2.5 concentrations. Chemical Transport Models (CTMs) have the potential to provide dynamic estimation of PM_2.5 concentrations by considering anthropogenic emissions and the transport and evolution of pollutants. We then comprehensively examine the mechanisms how major meteorological factors may impact the PM_2.5 concentrations, including the dispersion, growth, chemical production, photolysis, and deposition of PM_2.5. The feedback effects of PM_2.5 concentrations on meteorological factors are also carefully examined. Based on this review, suggestions on future research and major meteorological approaches for mitigating PM_2.5 pollution are made finally.

An Integrated Approach of Belief Rule Base and Deep Learning to Predict Air Pollution

Sensor data are gaining increasing global attention due to the advent of Internet of Things (IoT). Reasoning is applied on such sensor data in order to compute prediction. Generating a health warning that is based on prediction of atmospheric pollution, planning timely evacuation of people from vulnerable areas with respect to prediction of natural disasters, etc., are the use cases of sensor data stream where prediction is vital to protect people and assets. Thus, prediction accuracy is of paramount importance to take preventive steps and avert any untoward situation. Uncertainties of sensor data is a severe factor which hampers prediction accuracy. Belief Rule Based Expert System (BRBES), a knowledge-driven approach, is a widely employed prediction algorithm to deal with such uncertainties based on knowledge base and inference engine. In connection with handling uncertainties, it offers higher accuracy than other such knowledge-driven techniques, e.g., fuzzy logic and Bayesian probability theory. Contrarily, Deep Learning is a data-driven technique, which constitutes a part of Artificial Intelligence (AI). By applying analytics on huge amount of data, Deep Learning learns the hidden representation of data. Thus, Deep Learning can infer prediction by reasoning over available data, such as historical data and sensor data streams. Combined application of BRBES and Deep Learning can compute prediction with improved accuracy by addressing sensor data uncertainties while utilizing its discovered data pattern. Hence, this paper proposes a novel predictive model that is based on the integrated approach of BRBES and Deep Learning. The uniqueness of this model lies in the development of a mathematical model to combine Deep Learning with BRBES and capture the nonlinear dependencies among the relevant variables. We optimized BRBES further by applying parameter and structure optimization on it. Air pollution prediction has been taken as use case of our proposed combined approach. This model has been evaluated against two different datasets. One dataset contains synthetic images with a corresponding label of PM2.5 concentrations. The other one contains real images, PM2.5 concentrations, and numerical weather data of Shanghai, China. We also distinguished a hazy image between polluted air and fog through our proposed model. Our approach has outperformed only BRBES and only Deep Learning in terms of prediction accuracy.

Potential Impacts of Climate Change on Extreme Weather Events in the Niger Delta Part of Nigeria

The Niger Delta is the most climate-vulnerable region in Nigeria. Flooding events are recorded annually in settlements along the River Niger and its tributaries, inundating many towns and displacing people from their homes. In this study, climate change impacts from extreme meteorological events over the period 2010–2099 are predicted and analyzed. Four coupled model intercomparison project phase 5 (CMIP5) global climate models (GCMs) under respectively concentration pathways (RCP4.5 and RCP8.5) emission scenarios were used for climate change predictions. Standardized precipitation indices (SPI) of 1-month and 12-month time steps were used for extreme event assessment. Results from the climate change scenarios predict an increase in rainfall across all future periods and under both emission scenarios, with the highest projected increase during the last three decades of the century. Under the RCP8.5 emission scenario, the rainfall at Port Harcourt and Yenagoa Stations is predicted to increase by about 2.47% and 2.62% while the rainfall at Warri Station is predicted to increase by about 1.39% toward the end of the century. The 12-month SPI under RCP4.5 and RCP8.5 emission scenarios predict an exceedance in the extreme wet threshold (i.e., SPI > 2) during all future periods and across all study locations. These findings suggest an increasing risk of flooding within the projected periods. The finding can be useful to policymakers for the formulation and planning of flood mitigation and adaptation measures.

Relationships between Meteorological Parameters and Particulate Matter in Mae Hong Son Province, Thailand

Meteorological parameters play an important role in determining the prevalence of ambient particulate matter (PM) in the upper north of Thailand. Mae Hong Son is a province located in this region and which borders Myanmar. This study aimed to determine the relationships between meteorological parameters and ambient concentrations of particulate matter less than 10 µm in diameter (PM₁₀) in Mae Hong Son. Parameters were measured at an air quality monitoring station, and consisted of PM₁₀, carbon monoxide (CO), ozone (O₃), and meteorological factors, including temperature, rainfall, pressure, wind speed, wind direction, and relative humidity (RH). Nine years (2009⁻2017) of pollution and climate data obtained from the Thai Pollution Control Department (PCD) were used for analysis. The results of this study indicate that PM₁₀ is influenced by meteorological parameters; high concentration occurred during the dry season and northeastern monsoon seasons. Maximum concentrations were always observed in March. The PM₁₀ concentrations were significantly related to CO and O₃ concentrations and to RH, giving correlation coefficients of 0.73, 0.39, and -0.37, respectively (p-value < 0.001). Additionally, the hourly PM₁₀ concentration fluctuated within each day. In general, it was found that the reporting of daily concentrations might be best suited to public announcements and presentations. Hourly concentrations are recommended for public declarations that might be useful for warning citizens and organizations about air pollution. Our findings could be used to improve the understanding of PM₁₀ concentration patterns in Mae Hong Son and provide information to better air pollution measures and establish a warning system for the province.

Relevance Analysis on the Variety Characteristics of PM2.5 Concentrations in Beijing, China

Air pollution has become one of the most serious environmental problems in the world. Considering Beijing and six surrounding cities as main research areas, this study takes the daily average pollutant concentrations and meteorological factors from 2 December 2013 to 13 October 2017 into account and studies the spatial and temporal distribution characteristics and the relevant relationship of particulate matter smaller than 2.5 μm (PM2.5) concentrations in Beijing. Based on correlation analysis and geo-statistics techniques, the inter-annual, seasonal, and diurnal variation trends and temporal spatial distribution characteristics of PM2.5 concentration in Beijing are studied. The study results demonstrate that the pollutant concentrations in Beijing exhibit obvious seasonal and cyclical fluctuation patterns. Air pollution is more serious in winter and spring and slightly better in summer and autumn, with the spatial distribution of pollutants fluctuating dramatically in different seasons. The pollution in southern Beijing areas is more serious and the air quality in northern areas is better in general. The diurnal variation of air quality shows a typical seasonal difference and the daily variation of PM2.5 concentrations present a “W” type of mode with twin peaks. Besides emission and accumulation of local pollutants, air quality is easily affected by the transport effect from the southwest. The PM2.5 and PM10 concentrations measured from the city of Langfang are taken as the most important factors of surrounding pollution factors to PM2.5 in Beijing. The concentrations of PM10 and carbon monoxide (CO) concentrations in Beijing are the most significant local influencing factors to PM2.5 in Beijing. Extreme wind speeds and maximal wind speeds are considered to be the most significant meteorological factors affecting the transport of pollutants across the region. When the wind direction is weak southwest wind, the probability of air pollution is greater and when the wind direction is north, the air quality is generally better.

Effects of human activities and climate change on the reduction of visibility in Beijing over the past 36 years

Both climate change and intensive human activities are thought to have contributed to the impairment of atmospheric visibility in Beijing. But the detailed processes involved and relative roles of human activities and climate change have not been quantified. Optical extinction of aerosols, the inverse of meteorological visibility is especially sensitive to fine particles <1.0 μm. These submicron particles are considered more hazardous than larger ones in terms of cardiovascular and respiratory diseases. Here we used the aerosol optical extinction (inverse of visibility) as the indicator of submicron particles pollution to estimate its inter-annual variability from 1980 to 2015. Our results indicated that optical extinction experienced two different periods: a weakly increasing stage (1980-2005) and a rapidly increasing stage (2005-2015). We attributed the variations of optical extinction to the joint effects of human activities and climate change. Over the past 36 years, human activities played a leading role in the increase of optical extinction, with a positive contribution of 0.077 km^-1/10 y. While under the effects of climate change, optical extinction firstly decreased by 0.035 km^-1/10 y until 2005 and then increased by 0.087 km^-1/10 y. Detailed analysis revealed that the abrupt change (around 2005) of optical extinction resulted from the trend reversals of climate change. We found since 2005 the decreasing trend by 0.58 m·s^-1/10 y in wind speed, the growing trend at 8.69%/10 y in relative humidity and the declining trend by 2.72 hPa/10 y in atmospheric pressure have caused the rapid increase of optical extinction. In brief, the higher load of fine particles <1.0 μm in Beijing in recent decades could be associated with both human activities and climate change. Particularly after 2005, the adverse climate change aggravated the situation of submicron particles pollution.