Spatial distribution differences in PM2.5 concentration between heating and non-heating seasons in Beijing, China

2019 ◽  
Vol 248 ◽  
pp. 574-583 ◽  
Author(s):  
Wei Ji ◽  
Yong Wang ◽  
Dafang Zhuang
Keyword(s):  
Spatial Distribution ◽  
Beijing China ◽  
Pm2.5 Concentration

scholarly journals Atmospheric PM2.5 Prediction Using DeepAR Optimized by Sparrow Search Algorithm with Opposition-Based and Fitness-Based Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (7) ◽  
pp. 894
Author(s):  
Feng Jiang ◽  
Xingyu Han ◽  
Wenya Zhang ◽  
Guici Chen
Keyword(s):  
Search Algorithm ◽  
Recurrent Network ◽  
Learning Method ◽  
Probabilistic Forecasting ◽  
Atmospheric Concentration ◽  
Opposition Based Learning ◽  
Hourly Data ◽  
Beijing China ◽  
Important Significance ◽  
Pm2.5 Concentration

There is an important significance for human health in predicting atmospheric concentration precisely. However, due to the complexity and influence of contingency, atmospheric concentration prediction is a challenging topic. In this paper, we propose a novel hybrid learning method to make point and interval predictions of PM2.5 concentration simultaneously. Firstly, we optimize Sparrow Search Algorithm (SSA) by opposition-based learning, fitness-based learning, and Lévy flight. The experiments show that the improved Sparrow Search Algorithm (FOSSA) outperforms SSA-based algorithms. In addition, the improved Sparrow Search Algorithm (FOSSA) is employed to optimize the initial weights of probabilistic forecasting model with autoregressive recurrent network (DeepAR). Then, the FOSSA–DeepAR learning method is utilized to achieve the point prediction and interval prediction of PM2.5 concentration in Beijing, China. The performance of FOSSA–DeepAR is compared with other hybrid models and a single DeepAR model. Furthermore, hourly data of PM2.5 and O3 concentration in Taian of China, O3 concentration in Beijing, China are used to verify the effectiveness and robustness of the proposed FOSSA–DeepAR learning method. Finally, the empirical results illustrate that the proposed FOSSA–DeepAR learning model can achieve more efficient and accurate predictions in both interval and point prediction.

The Spatial Distribution and Clustering of Convention Facilities in Beijing, China

2013 ◽  
Vol 19 (9) ◽  
pp. 1070-1090 ◽  
Author(s):  
Li Cong ◽  
Bihu Wu ◽  
Alastair M. Morrison ◽  
Kou Xi
Keyword(s):  
Spatial Distribution ◽  
Beijing China

scholarly journals Spatiotemporal Distribution of PM2.5 and O3 and Their Interaction During the Summer and Winter Seasons in Beijing, China

2018 ◽  
Vol 10 (12) ◽  
pp. 4519 ◽  
Author(s):  
Hui Zhao ◽  
Youfei Zheng ◽  
Chen Li
Keyword(s):  
Traffic Monitoring ◽  
Diurnal Variations ◽  
Quality Monitoring ◽  
Air Quality Monitoring ◽  
Spatiotemporal Variations ◽  
Spatial Trend ◽  
The North ◽  
Northern Areas ◽  
Beijing China ◽  
Pm2.5 Concentration

This study analyzed the spatiotemporal variations in PM2.5 and O3, and explored their interaction in the summer and winter seasons in Beijing. To this aim, hourly PM2.5 and O3 data for 35 air quality monitoring sites were analyzed during the summer and winter of 2016. Results suggested that the highest PM2.5 concentration and the lowest O3 concentration were observed at traffic monitoring sites during the two seasons. A statistically significant (p < 0.05) different diurnal variation of PM2.5 was observed between the summer and winter seasons, with higher concentrations during daytime summer and nighttime winter. Diurnal variations of O3 concentrations during the two seasons showed a single peak, occurring at 16:00 and 15:00 in summer and winter, respectively. PM2.5 presented a spatial pattern with higher concentrations in southern Beijing than in northern areas, particularly evident during wintertime. On the contrary, O3 concentrations presented a decreasing spatial trend from the north to the south, particularly evident during summer. In addition, we found that PM2.5 concentrations were positively correlated (p < 0.01, r = 0.57) with O3 concentrations in summer, but negatively correlated (p < 0.01, r = −0.72) with O3 concentrations in winter.

scholarly journals Influence of the Spatial Distribution Pattern of Buildings on the Distribution of PM2.5 Concentration

2021 ◽  
Author(s):  
Zhixuan Zhang ◽  
Baoyan Shan ◽  
Qikai Lin ◽  
Yanqiu Chen ◽  
Xinwei Yu
Keyword(s):  
Spatial Distribution ◽  
Standard Deviation ◽  
Spatial Structure ◽  
Distribution Pattern ◽  
Distribution Patterns ◽  
Spatial Distribution Pattern ◽  
Urban Spatial Structure ◽  
Coverage Ratio ◽  
The Mean ◽  
Pm2.5 Concentration

Abstract The spatial distribution pattern of buildings is an entry point for controlling the diffusion of pollution particles at an urban spatial structure scale. In this study, we adopted ordinary kriging interpolation and other methods to study the spatial distribution pattern of PM2.5 and constructed urban spatial structure indexes based on building distribution patterns to reveal the influence of building spatial distribution patterns on PM2.5 concentration across the study area and at different elevations. The present study suggests that: (1) Topographic elevation is an important factor influencing the distribution of PM2.5; the correlation coefficient reaches −0.761 and exceeds the 0.001 confidence level. As the elevation increases, the urban spatial structure indexes show significant correlations with PM2.5, and the regularity becomes stronger. (2) The PM2.5 concentration is negatively correlated with the mean and standard deviation of the DEM, the mean and maximum absolute building height, the outdoor activity area, and the average distance between adjacent buildings; and is positively correlated with the sum of the building base area, the building coverage ratio, the space area, the building coverage ratio, the space occupation ratio, and the sum of the building volume. These urban spatial structure indexes are important factors affecting PM2.5 concentration and distribution and should be considered in urban planning. (3) Spatio-temporal differences in PM2.5 concentration and distribution were found at different elevation and time ranges. Indexes, such as the average building height, the average building base area, the sum of the building volume, and the standard deviation of building volume experienced significant changes. Higher PM2.5 concentration yielded a more significant influence of urban spatial structure indexes on PM2.5 distribution. More discrete spatial distributions of PM2.5 yielded weaker correlations between PM2.5 concentrations and the urban spatial structure indexes.

scholarly journals Temporal-spatial Analysis of Contributors’ Mapping Behavior for Building Data in OpenStreetMap

2019 ◽  
Vol 2 ◽  
pp. 1-10
Author(s):  
Jin Xu ◽  
Qi Zhou
Keyword(s):  
Spatial Distribution ◽  
Spatial Analysis ◽  
Spatial Patterns ◽  
Volunteered Geographic Information ◽  
Geographic Information ◽  
Developing Regions ◽  
Temporal And Spatial Patterns ◽  
Temporal And Spatial ◽  
One Year ◽  
Beijing China

<p><strong>Abstract.</strong> Volunteered Geographic Information (VGI) crowdsourced from volunteering posts, is closely related to contributors’ mapping behavior. As the most noticeable VGI source, OpenStreetMap (OSM) is one of the most studied objectives in VGI and data contributors. In this paper, temporal-spatial analysis is applied in seeking the temporal and spatial patterns of the number of buildings and contributors in Beijing, China. Temporal changes of the number of updated buildings, and the population of total, new and quitted contributors, were interpreted, as well as the spatial distribution of updated buildings, participated contributors, and frequency of updates. The result suggests that the number of updated buildings, participated contributors, new and quitted contributors are growing. Buildings are mostly updated by a small number of contributors, the majority of which did not participated in mapping in the previous year. Most contributors update buildings for one year without succeeding till the next. Contributors are interested in updating a large amount of buildings frequently around landmarks, commercial districts, universities, and transit hubs. They update buildings at an expanding range and an increasing density, but their attentions do not necessarily bring large quantity of building updates. In general, OSM buildings in developing regions with less complete database are updated under similar patterns as developed regions where data are much more complete.</p>

scholarly journals Field Evaluation of Low-Cost Particulate Matter Sensors in Beijing

Sensors ◽  
2020 ◽  
Vol 20 (16) ◽  
pp. 4381 ◽  
Author(s):  
Han Mei ◽  
Pengfei Han ◽  
Yinan Wang ◽  
Ning Zeng ◽  
Di Liu ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Low Cost ◽  
Field Evaluation ◽  
Dust Storms ◽  
Relative Bias ◽  
Miniaturized Sensors ◽  
Beijing China ◽  
Pm2.5 Concentration

Numerous particulate matter (PM) sensors with great development potential have emerged. However, whether the current sensors can be used for reliable long-term field monitoring is unclear. This study describes the research and application prospects of low-cost miniaturized sensors in PM2.5 monitoring. We evaluated five Plantower PMSA003 sensors deployed in Beijing, China, over 7 months (October 2019 to June 2020). The sensors tracked PM2.5 concentrations, which were compared to the measurements at the national control monitoring station of the Ministry of Ecology and Environment (MEE) at the same location. The correlations of the data from the PMSA003 sensors and MEE reference monitors (R2 = 0.83~0.90) and among the five sensors (R2 = 0.91~0.98) indicated a high accuracy and intersensor correlation. However, the sensors tended to underestimate high PM2.5 concentrations. The relative bias reached −24.82% when the PM2.5 concentration was >250 µg/m3. Conversely, overestimation and high errors were observed during periods of high relative humidity (RH > 60%). The relative bias reached 14.71% at RH > 75%. The PMSA003 sensors performed poorly during sand and dust storms, especially for the ambient PM10 concentration measurements. Overall, this study identified good correlations between PMSA003 sensors and reference monitors. Extreme field environments impact the data quality of low-cost sensors, and future corrections remain necessary.

Sign in / Sign up

Export Citation Format

Share Document