Pattern of Spatial Distribution and Temporal Variation of Atmospheric Pollutants during 2013 in Shenzhen, China

Xia, Xiaolin; Qi, Qingwen; Liang, Hong; Zhang, An; Jiang, Lili; Ye, Yanjun; Liu, Chanfang; Huang, Yuanfeng

doi:10.3390/ijgi6010002

Cited by 24 publications

(20 citation statements)

References 36 publications

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“…This concentration in Abidjan is comparable to those measured in Bamako (16.2 ppb) and Shenzhen (20.7 ppb) reported by Adon et al (2016) and Xia et al (2017). However, it remains lower than the concentrations obtained in Dakar (31.7 ppb) and Al Ain in the Middle East (31.5 ppb) and reported respectively by Adon et al (2016) and Salem et al (2009).…”

Section: Traffic and Urban Sitessupporting

confidence: 49%

A pilot study of gaseous pollutants' measurement (NO2, SO2, NH3, HNO3 and O3) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities

Bahino¹,

Yoboue²,

Galy‐Lacaux

et al. 2018

Atmos. Chem. Phys.

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Abstract. This work is part of the DACCIWA FP7 project (Dynamics-Aerosol-Chemistry-Cloud Interactions in West Africa) in the framework of the Work Package 2 "Air Pollution and Health". This study aims to characterize urban air pollution levels through the measurement of NO 2 , SO 2 , NH 3 , HNO 3 and O 3 in Abidjan, the economic capital of Côte d'Ivoire. Measurements of inorganic gaseous pollutants, i.e. NO 2 , SO 2 , NH 3 , HNO 3 and O 3 were performed in Abidjan during an intensive campaign within the dry season (15 December 2015 to 16 February 2016), using INDAAF (International Network to study Deposition and Atmospheric chemistry in AFrica) passive samplers exposed in duplicate for 2-week periods. Twenty-one sites were selected in the district of Abidjan to be representative of various anthropogenic and natural sources of air pollution in the city. Results from this intensive campaign show that gas concentrations are strongly linked to surrounding pollution sources and show a high spatial variability. Also, NH 3 , NO 2 and O 3 gases were present at relatively higher concentrations at all the sites. NH 3 average concentrations varied between 9.1 ± 1.7 ppb at a suburban site and 102.1 ± 9.1 ppb at a domestic fires site. NO 2 mean concentration varied from 2.7 ± 0.1 ppb at a suburban site to 25.0 ± 1.7 ppb at an industrial site. Moreover, we measured the highest O 3 concentration at the two coastal sites of Gonzagueville and Félix-Houphouët-Boigny International Airport located in the southeast of the city, with average concentrations of 19.1 ± 1.7 and 18.8 ± 3.0 ppb, respectively. The SO 2 average concentration never exceeded 7.2 ± 1.2 ppb over all the sites, with 71.5 % of the sampling sites showing concentrations ranging between 0.4 and 1.9 ppb. The HNO 3 average concentration ranged between 0.2 and 1.4 ppb. All these results were combined with meteorological parameters to provide the first mapping of gaseous pollutants on the scale of the district of Abidjan using geostatistical analysis (ArcGIS software). Spatial distribution results emphasize the importance of the domestic fires source and the significant impact of the traffic emissions on the scale of the city. In addition, in this work we propose a first overview of gaseous SO 2 and NO 2 concentrations on the scale of several African cities by comparing literature to our values. The daily SO 2 standard of World Health Organization (WHO) is exceeded in most of the cities reported in the overview, with concentrations ranging from 0.2 to 3662 µg m −3 . Annual NO 2 concentrations ranged from 2 to 175 µg m −3 , which are lower than the WHO threshold. As a conclusion, this study constitutes an original database to characterize urban air pollution and a first attempt towards presenting a spatial distribution of the pollution levels at the scale of the metropolis of Abidjan. This work should draw the attention of the African public authorities to the necessity of building an air quality monitoring network in order to (1) to define national standards and to bette...

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Section: Traffic and Urban Sitessupporting

confidence: 49%

A pilot study of gaseous pollutants' measurement (NO2, SO2, NH3, HNO3 and O3) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities

Bahino¹,

Yoboue²,

Galy‐Lacaux

et al. 2018

Atmos. Chem. Phys.

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“…Knowledge regarding the subtype or category of a future event is vital if decision makers are to achieve accurate and optimal resource allocation. For example, Figure 1 shows the percentage of six pollutant subtypes that feature in air pollution events based on the most frequently detected primary pollutants in Shenzhen, China in Summer 2013 (Xia et al 2016). Local Environmental Monitoring Centers try to identify which pollutant source causing the most harm to public health and take appropriate action.…”

Section: Introductionmentioning

confidence: 99%

“…Relative amounts of six air pollutant subtypes in 10 districts in Shenzhen, China, 2013(Xia et al 2016).…”

mentioning

confidence: 99%

Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting

Gao

Zhao

Wu³

et al. 2019

AAAI

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Due to the potentially significant benefits for society, forecasting spatio-temporal societal events is currently attracting considerable attention from researchers. Beyond merely predicting the occurrence of future events, practitioners are now looking for information about specific subtypes of future events in order to allocate appropriate amounts and types of resources to manage such events and any associated social risks. However, forecasting event subtypes is far more complex than merely extending binary prediction to cover multiple classes, as 1) different locations require different models to handle their characteristic event subtype patterns due to spatial heterogeneity; 2) historically, many locations have only experienced a incomplete set of event subtypes, thus limiting the local model's ability to predict previously "unseen" subtypes; and 3) the subtle discrepancy among different event subtypes requires more discriminative and profound representations of societal events. In order to address all these challenges concurrently, we propose a Spatial Incomplete Multi-task Deep leArning (SIMDA) framework that is capable of effectively forecasting the subtypes of future events. The new framework formulates spatial locations into tasks to handle spatial heterogeneity in event subtypes, and learns a joint deep representation of subtypes across tasks. Furthermore, based on the "first law of geography", spatiallyclosed tasks share similar event subtype patterns such that adjacent tasks can share knowledge with each other effectively. Optimizing the proposed model amounts to a new nonconvex and strongly-coupled problem, we propose a new algorithm based on Alternating Direction Method of Multipliers (ADMM) that can decompose the complex problem into subproblems that can be solved efficiently. Extensive experiments on six real-world datasets demonstrate the effectiveness and efficiency of the proposed model.

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“…The BME method takes many types of data and different types of knowledge bases into spatial interpolation. These data and information are divided into general knowledge (K G ) and site-specific knowledge (K S ) [23][24][25][26][27]. The Ks is composed of soft data and hard data.…”

Section: Spatial Interpolationmentioning

confidence: 99%

Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China

Zhong

Wang

et al. 2017

IJGI

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Abstract:With the continuous progress of human production and life, air quality has become the focus of attention. In this paper, Beijing, Tianjin, Hebei, Shanxi, Shandong and Henan provinces were taken as the study area, where there are 58 air quality monitoring stations from which daily and monthly data are obtained. Firstly, the temporal characteristics of the air quality index (AQI) are explored. Then, the spatial distribution of the AQI is mapped by the inverse distance weighted (IDW) method, the ordinary kriging (OK) method and the Bayesian maximum entropy (BME) method. Additionally, cross-validation is utilized to evaluate the mapping results of these methods with two indexes: mean absolute error and root mean square interpolation error. Furthermore, the correlation analysis of meteorological factors, including precipitation anomaly percentage, precipitation, mean wind speed, average temperature, average water vapor pressure and average relative humidity, potentially affecting the AQI was carried out on both daily and monthly scales. In the study area and period, AQI shows a clear periodicity, although overall, it has a downward trend. The peak of AQI appeared in November, December and January. BME interpolation has a higher accuracy than OK. IDW has the maximum error. Overall, the AQI of winter (November), spring (February) is much worse than summer (May) and autumn (August). Additionally, the air quality has improved during the study period. The most polluted areas of air quality are concentrated in Beijing, the southern part of Tianjin, the central-southern part of Hebei, the central-northern part of Henan and the western part of Shandong. The average wind speed and average relative humidity have real correlation with AQI. The effect of meteorological factors such as wind, precipitation and humidity on AQI is putative to have temporal lag to different extents. AQI of cities with poor air quality will fluctuate greater than that of others when weather changes and has higher correlation with meteorological factors.

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Pattern of Spatial Distribution and Temporal Variation of Atmospheric Pollutants during 2013 in Shenzhen, China

Cited by 24 publications

References 36 publications

A pilot study of gaseous pollutants' measurement (NO<sub>2</sub>, SO<sub>2</sub>, NH<sub>3</sub>, HNO<sub>3</sub> and O<sub>3</sub>) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities

A pilot study of gaseous pollutants' measurement (NO<sub>2</sub>, SO<sub>2</sub>, NH<sub>3</sub>, HNO<sub>3</sub> and O<sub>3</sub>) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities

Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting

Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China

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Pattern of Spatial Distribution and Temporal Variation of Atmospheric Pollutants during 2013 in Shenzhen, China

Cited by 24 publications

References 36 publications

A pilot study of gaseous pollutants' measurement (NO&lt;sub&gt;2&lt;/sub&gt;, SO&lt;sub&gt;2&lt;/sub&gt;, NH&lt;sub&gt;3&lt;/sub&gt;, HNO&lt;sub&gt;3&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt;) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities

A pilot study of gaseous pollutants' measurement (NO&lt;sub&gt;2&lt;/sub&gt;, SO&lt;sub&gt;2&lt;/sub&gt;, NH&lt;sub&gt;3&lt;/sub&gt;, HNO&lt;sub&gt;3&lt;/sub&gt; and O&lt;sub&gt;3&lt;/sub&gt;) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities

Incomplete Label Multi-Task Deep Learning for Spatio-Temporal Event Subtype Forecasting

Mapping Comparison and Meteorological Correlation Analysis of the Air Quality Index in Mid-Eastern China

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Product

Resources

About

A pilot study of gaseous pollutants' measurement (NO<sub>2</sub>, SO<sub>2</sub>, NH<sub>3</sub>, HNO<sub>3</sub> and O<sub>3</sub>) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities

A pilot study of gaseous pollutants' measurement (NO<sub>2</sub>, SO<sub>2</sub>, NH<sub>3</sub>, HNO<sub>3</sub> and O<sub>3</sub>) in Abidjan, Côte d'Ivoire: contribution to an overview of gaseous pollution in African cities