scholarly journals Roadside Air Quality Forecasting in Shanghai with a Novel Sequence-to-Sequence Model

2020 ◽  
Vol 17 (24) ◽  
pp. 9471
Author(s):  
Dongsheng Wang ◽  
Hong-Wei Wang ◽  
Chao Li ◽  
Kai-Fa Lu ◽  
Zhong-Ren Peng ◽  
...  
Keyword(s):  
Air Quality ◽  
Air Pollutants ◽  
Traffic Management ◽  
Fine Particulate Matter ◽  
Temporal Variations ◽  
Learning Ability ◽  
Observation Data ◽  
Deterministic Models ◽  
Co Concentrations ◽  
Air Quality Forecasting

The establishment of an effective roadside air quality forecasting model provides important information for proper traffic management to mitigate severe pollution, and for alerting resident’s outdoor plans to minimize exposure. Current deterministic models rely on numerical simulation and the tuning of parameters, and empirical models present powerful learning ability but have not fully considered the temporal periodicity of air pollutants. In order to take the periodicity of pollutants into empirical air quality forecasting models, this study evaluates the temporal variations of air pollutants and develops a novel sequence to sequence model with weekly periodicity to forecast air quality. Two-year observation data from Shanghai roadside air quality monitoring stations are employed to support analyzing and modeling. The results conclude that the fine particulate matter (PM2.5) and carbon monoxide (CO) concentrations show obvious daily and weekly variations, and the temporal patterns are nearly consistent with the periodicity of traffic flow in Shanghai. Compared with PM2.5, the CO concentrations are more affected by traffic variation. The proposed model outperforms the baseline model in terms of accuracy, and presents a higher linear consistency in PM2.5 prediction and lower errors in CO prediction. This study could assist environmental researchers to further improve the technologies for urban air quality forecasting, and serve as tools for supporting policymakers to implement related traffic management and emission control policies.

scholarly journals Characteristics and sources of atmospheric pollutants in typical inland cities in arid regions of central Asia: A case study of Urumqi city

PLoS ONE ◽  
2021 ◽  
Vol 16 (4) ◽  
pp. e0249563
Author(s):  
Zongying Li ◽  
Yao Wang ◽  
Zhonglin Xu ◽  
Yue’e Cao
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Central Asia ◽  
Air Pollutants ◽  
Trajectory Analysis ◽  
Atmospheric Pollutants ◽  
Observation Data ◽  
Backward Trajectory ◽  
Concentration Changes ◽  
Co Concentrations

The arid zone of central Asia secluded inland and has the typical features of the atmosphere. Human activities have had a significant impact on the air quality in this region. Urumqi is a key city in the core area of the Silk Road and an important economic center in Northwestern China. The urban environment is playing an increasingly important role in regional development. To study the characteristics and influencing factors of the main atmospheric pollutants in Urumqi, this study selected Urumqi’s daily air quality index (AQI) data and observation data of six major pollutants including fine particulate matter (PM2.5), breathable particulate matter (PM10), sulfur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3_8h) from 2014 to 2018 in conjunction with meteorological data to use a backward trajectory analysis method to study the main characteristics of atmospheric pollutants and their sources in Urumqi from 2014 to 2018. The results showed that: (1) From 2014 to 2018, the annual average of PM2.5, PM10, SO2, NO2 and CO concentrations showed a downward trend, and O3_8h concentrations first increased, then decreased, and then increased, reaching the highest value in 2018 (82.15 μg·m-3); The seasonal changes of PM2.5, PM10, SO2, NO2 and CO concentrations were characterized by low values in summer and fall seasons and high values in winter and spring seasons. The concentration of O3_8h, however, was in the opposite trend, showing the high values in summer and fall seasons, and low values in winter and spring seasons. From 2014 to 2018, with the exception of O3_8h, the concentration changes of the other five major air pollutants were high in December, January, and February, and low in May, June, and July; the daily changes showed a “U-shaped” change during the year. The high-value areas of the "U-shaped" mode formed around the 50th day and the 350th day. (2) The high-value area of AQI was from the end of fall (November) to the beginning of the following spring (March), and the low-value area was from April to October. It showed a U-shaped change trend during the year and the value was mainly distributed between 50 and 100. (3) The concentrations of major air pollutants in Urumqi were significantly negatively correlated with precipitation, temperature, and humidity (P<0.01), and had the highest correlation coefficients with temperature. (4) Based on the above analysis results, this study analyzed two severe pollution events from late November to early December. Analysis showed that the PM2.5/PM10 ratio in two events remained at about 0.1 when the pollution occurred, but was higher before and after the pollution (up to 1.46). It was shown that the pollution was a simple sandstorm process. Backward trajectory analysis clustered the airflow trajectories reaching Urumqi into 4 categories, and the trajectories from central Asia contributed the maximum values of average PM2.5 and PM10 concentrations.

scholarly journals Description and Evaluation of the Fine Particulate Matter Forecasts in the NCAR Regional Air Quality Forecasting System

Atmosphere ◽  
2021 ◽  
Vol 12 (3) ◽  
pp. 302
Author(s):  
Rajesh Kumar ◽  
Piyush Bhardwaj ◽  
Gabriele Pfister ◽  
Carl Drews ◽  
Shawn Honomichl ◽  
...  
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Information Dissemination ◽  
Fine Particulate Matter ◽  
Model Errors ◽  
Air Quality Forecast ◽  
Fine Particulate ◽  
Regional Air Quality ◽  
Forecasting System ◽  
Air Quality Forecasting

This paper describes a quasi-operational regional air quality forecasting system for the contiguous United States (CONUS) developed at the National Center for Atmospheric Research (NCAR) to support air quality decision-making, field campaign planning, early identification of model errors and biases, and support the atmospheric science community in their research. This system aims to complement the operational air quality forecasts produced by the National Oceanic and Atmospheric Administration (NOAA), not to replace them. A publicly available information dissemination system has been established that displays various air quality products, including a near-real-time evaluation of the model forecasts. Here, we report the performance of our air quality forecasting system in simulating meteorology and fine particulate matter (PM2.5) for the first year after our system started, i.e., 1 June 2019 to 31 May 2020. Our system shows excellent skill in capturing hourly to daily variations in temperature, surface pressure, relative humidity, water vapor mixing ratios, and wind direction but shows relatively larger errors in wind speed. The model also captures the seasonal cycle of surface PM2.5 very well in different regions and for different types of sites (urban, suburban, and rural) in the CONUS with a mean bias smaller than 1 µg m−3. The skill of the air quality forecasts remains fairly stable between the first and second days of the forecasts. Our air quality forecast products are publicly available at a NCAR webpage. We invite the community to use our forecasting products for their research, as input for urban scale (<4 km), air quality forecasts, or the co-development of customized products, just to name a few applications.

scholarly journals Ensemble Predictions of Air Pollutants in China in 2013 for Health Effects Studies Using WRF/CMAQ Modeling System with Four Emission Inventories

2017 ◽  
Author(s):  
Jianlin Hu ◽  
Xun Li ◽  
Lin Huang ◽  
Qi Ying ◽  
Qiang Zhang ◽  
...  
Keyword(s):  
Air Quality ◽  
Health Effects ◽  
Air Pollutants ◽  
Emission Inventory ◽  
Model Performance ◽  
Observation Data ◽  
Emission Inventories ◽  
Air Quality Model ◽  
Quality Model ◽  
Future Health

Abstract. Accurate exposure estimates are required for health effects analyses of severe air pollution in China. Chemical transport models (CTMs) are widely used tools to provide detailed information of spatial distribution, chemical composition, particle size fractions, and source origins of pollutants. The accuracy of CTMs' predictions in China is largely affected by the uncertainties of public available emission inventories. The Community Multi-scale Air Quality model (CMAQ) with meteorological inputs from the Weather Research and Forecasting model (WRF) were used in this study to simulate air quality in China in 2013. Four sets of simulations were conducted with four different anthropogenic emission inventories, including the Multi-resolution Emission Inventory for China (MEIC), the Emission Inventory for China by School of Environment at Tsinghua University (SOE), the Emissions Database for Global Atmospheric Research (EDGAR), and the Regional Emission inventory in Asia version 2 (REAS2). Model performance was evaluated against available observation data from 422 sites in 60 cities across China. Model predictions of O3 and PM2.5 with the four inventories generally meet the criteria of model performance, but difference exists in different pollutants and different regions among the inventories. Ensemble predictions were calculated by linearly combining the results from different inventories under the constraint that sum of the squared errors between the ensemble results and the observations from all the cities was minimized. The ensemble annual concentrations show improved agreement with observations in most cities. The mean fractional bias (MFB) and mean fractional errors (MFE) of the ensemble predicted annual PM2.5 at the 60 cities are −0.11 and 0.24, respectively, which are better than the MFB (−0.25–−0.16) and MFE (0.26–0.31) of individual simulations. The ensemble annual 1-hour peak O3 (O3-1 h) concentrations are also improved, with mean normalized bias (MNB) of 0.03 and mean normalized errors (MNE) of 0.14, compared to MNB of 0.06–0.19 and MNE of 0.16–0.22 of the individual predictions. The ensemble predictions agree better with observations with daily, monthly, and annual averaging times in all regions of China for both PM2.5 and O3-1 h. The study demonstrates that ensemble predictions by combining predictions from individual emission inventories can improve the accuracy of predicted temporal and spatial distributions of air pollutants. This study is the first ensemble model study in China using multiple emission inventories and the results are publicly available for future health effects studies.

scholarly journals Contributions of Trans-boundary Transport to the Summertime Air Quality in Beijing, China

2016 ◽  
Author(s):  
Jiarui Wu ◽  
Guohui Li ◽  
Junji Cao ◽  
Naifang Bei ◽  
Yichen Wang ◽  
...  
Keyword(s):  
Air Quality ◽  
Control Strategies ◽  
Dominant Role ◽  
Fine Particulate Matter ◽  
Temporal Variations ◽  
Pollutant Emissions ◽  
Beijing China ◽  
Local Emissions ◽  
Factor Separation ◽  
Good Agreement

Abstract. In the present study, the WRF-CHEM model is used to evaluate the contributions of trans-boundary transport to the air quality in Beijing during a persistent air pollution episode from 5 to 14 July 2015 in Beijing-Tianjin-Hebei (BTH), China. Generally, the predicted temporal variations and spatial distributions of PM2.5 (fine particulate matter), O3 (ozone), and NO2 are in good agreement with observations in BTH. The WRF-CHEM model also reproduces reasonably well the temporal variations of aerosol species compared to measurements in Beijing. The factor separation approach is employed to evaluate the contributions of trans-boundary transport of emissions outside of Beijing to the PM2.5 and O3 levels in Beijing. On average, in the afternoon during the simulation episode, the pure local emissions contribute 22.4 % to the O3 level in Beijing, less than 36.6 % from pure emissions outside of Beijing. The O3 concentrations in Beijing are decreased by 5.1 % in the afternoon due to interactions of local emissions with those outside of Beijing. The pure emissions outside of Beijing play a dominant role in the PM2.5 level in Beijing, with a contribution of 61.5 %, much more than 13.7 % from pure Beijing local emissions. The emissions interactions enhance the PM2.5 concentrations in Beijing, with a contribution of 5.9 %. Therefore, the air quality in Beijing is primarily determined by the trans-boundary transport of emissions outside of Beijing during summertime, showing that the cooperation with neighboring provinces to mitigate pollutant emissions is a key for Beijing to improve air quality. Considering the uncertainties in the emission inventory and the meteorological field simulations, further studies need to be performed to improve the WRF-CHEM model simulations to reasonably evaluate trans-boundary transport contributions to the air quality in Beijing for supporting the design and implementation of emission control strategies.

Improving air quality forecasting with the assimilation of GOCI AOD retrievals

2020 ◽  
Author(s):  
Soyoung Ha ◽  
Zhiquan Liu
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
South Korea ◽  
Positive Impact ◽  
Fine Particulate Matter ◽  
Three Dimensional ◽  
Basis Set ◽  
Upstream Region ◽  
The Impact ◽  
Air Quality Forecasting

&lt;p&gt;The Korean Geostationary Ocean Color Imager (GOCI) satellite has monitored the East Asian region in high temporal and spatial resolution every day for the last decade, providing unprecedented information on air pollutants over the upstream region of the Korean peninsula. In this study, the GOCI Aerosol optical depth (AOD), retrieved at 550 nm wavelength, is assimilated to ameliorate the analysis quality, thereby making systematic improvements on air quality forecasting in South Korea. For successful data assimilation, GOCI retrievals are carefully investigated and processed based on data characteristics. The preprocessed data are then assimilated in the three-dimensional variational data assimilation (3DVAR) technique for the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem). Over the Korea-United States Air Quality (KORUS-AQ) period (May 2016), the impact of GOCI AOD on the accuracy of air quality forecasting is examined by comparing with other observations including Moderate Resolution Imaging Spectroradiometer (MODIS) sensors and fine particulate matter (PM2.5) observations at the surface. Consistent with previous studies, the assimilation of surface PM2.5 concentrations alone systematically underestimates surface PM2.5 and its positive impact lasts mainly for about 6 h. When GOCI AOD retrievals are assimilated with surface PM2.5 observations, however, the negative bias is diminished and forecasts are improved up to 24 h, with the most significant contributions to the prediction of heavy pollution events over South Korea. The talk will be finished with an introduction of our ongoing efforts on developing the assimilation capability for more sophisticated aerosol schemes such as Model for Simulating Aerosol Interactions and Chemistry (MOSAIC) and the Modal Aerosol Dynamics Model for Europe (MADE)-Volatility basis set (VBS).&lt;/p&gt;

scholarly journals Development of a deep neural network for predicting 6-hour average PM<sub>2.5</sub> concentrations up to two subsequent days using various training data

2021 ◽  
Author(s):  
Jeong-Beom Lee ◽  
Jae-Bum Lee ◽  
Youn-Seo Koo ◽  
Hee-Yong Kwon ◽  
Min-Hyeok Choi ◽  
...  
Keyword(s):  
Neural Network ◽  
Air Quality ◽  
Deep Neural Network ◽  
Model Performance ◽  
Prediction Performance ◽  
Training Data ◽  
Forecasting Model ◽  
Observation Data ◽  
Forecast Data ◽  
Air Quality Forecasting

Abstract. This study aims to develop a deep neural network (DNN) model as an artificial neural network (ANN) for the prediction of 6-hour average fine particulate matter (PM2.5) concentrations for a three-day period—the day of prediction (D+0), one day after prediction (D+1) and two days after prediction (D+2)—using observation data and forecast data obtained via numerical models. The performance of the DNN model was comparatively evaluated against that of the currently operational Community Multiscale Air Quality (CMAQ) modelling system for air quality forecasting in South Korea. In addition, the effect on predictive performance of the DNN model on using different training data was analyzed. For the D+0 forecast, the DNN model performance was superior to that of the CMAQ model, and there was no significant dependence on the training data. For the D+1 and D+2 forecasts, the DNN model that used the observation and forecast data (DNN-ALL) outperformed the CMAQ model. The root-mean-squared error (RMSE) of DNN-ALL was lower than that of the CMAQ model by 2.2 μgm−3, and 3.0 μgm−3 for the D+1 and D+2 forecasts, respectively, because the overprediction of higher concentrations was curtailed. An IOA increase of 0.46 for D+1 prediction and 0.59 for the D+2 prediction was observed in case of the DNN-ALL model compared to the IOA of the DNN model that used only observation data (DNN-OBS). In additionally, An RMSE decrease of 7.2 μgm−3 for the D+1 prediction and 6.3 μgm−3 for the D+2 prediction was observed in case of the DNN-ALL model, compared to the RMSE of DNN-OBS, indicating that the inclusion of forecast data in the training data greatly affected the DNN model performance. Considering the prediction of the 6-hour average PM2.5 concentration, the 8.8 μgm−3 RMSE of the DNN-ALL model was 2.7 μgm−3 lower than that of the CMAQ model, indicating the superior prediction performance of the former. These results suggest that the DNN model could be utilized as a better-performing air quality forecasting model than the CMAQ, and that observation data plays an important role in determining the prediction performance of the DNN model for D+0 forecasting, while prediction data does the same for D+1 and D+2 forecasting. The use of the proposed DNN model as a forecasting model may result in a reduction in the economic losses caused by pollution-mitigation policies and aid better protection of public health.

scholarly journals Temporal analysis of air pollutants in the most affected monsoon region

2021 ◽  
Vol 23 (1) ◽  
pp. 229
Author(s):  
Omar Kairan ◽  
Nur Nasehah Zainudin ◽  
Nurul Hasya Mohd Hanafiah ◽  
Nur Emylia Arissa Mohd Jafri ◽  
Fukayhah Fatiha @Suhami ◽  
...  
Keyword(s):  
Air Pollution ◽  
Air Quality ◽  
Air Pollutants ◽  
Monsoon Season ◽  
Secondary Data ◽  
Ambient Air ◽  
Temporal Analysis ◽  
Quality Standard ◽  
Temporal Variations ◽  
Air Pollutant

Air pollution has become an issue at all rates in the world. In Malaysia, there is a system is known as air quality index (API) used to indicate the overall air quality in the country where the air pollutants include or the new ambient air quality standard are sulphur dioxide (SO2), nitrogen dioxide (NO2), carbon monoxide (CO), ozone (O3) and particulate matter with size less than 10 (PM10). The concentration levels of the air pollutants were said to be affected by the monsoon changes. Therefore, this study is conducted to examine the existence of temporal variations of each air pollutant then identify the differences of each air pollutants concentration in temporal variations. This study uses secondary data where data that has been retrieved from the Department of Environment (DOE) where it is data of air pollution specifically for Kota Bharu, kelantan records. Hierarchical agglomerative cluster analysis was conducted to group monthly air quality. As a conclusion, the study can conclude that the five air pollutants grouped into several different monthly clusters mostly representing the two main monsoon seasons. Mostly air pollutant varied accordingly towards the monsoon season. During the southwestern monsoon, air pollutant concentration tends to higher compare to the northeastern monsoon with mostly due to meteorological factors.

Satellite-based observations of ground-level fine particulate matter and comparison to a regional air quality model

2020 ◽  
Author(s):  
Jana Handschuh ◽  
Frank Baier ◽  
Thilo Erbertseder ◽  
Martijn Schaap
Keyword(s):  
Particulate Matter ◽  
Air Quality ◽  
Air Pollutants ◽  
Fine Particulate Matter ◽  
Ground Level ◽  
In Situ Measurements ◽  
Satellite Observations ◽  
Optical Parameters ◽  
Fine Particulate

&lt;p&gt;Particulate matter and other air pollutants have become an increasing burden on the environment and human health. Especially in metropolitan and high-traffic areas, air quality is often remarkably reduced. For a better understanding of the air quality in specific areas, which is of great environment-political interest, data with high resolution in space and time is required. The combination of satellite observations and chemistry-transport-modelling has proven to give a good database for assessments and analyses of air pollution. In contrast to sample in-situ measurements, satellite observations provide area-wide coverage &amp;#8203;&amp;#8203;of measurements and thus the possibility for an almost gapless mapping of actual air pollutants. For a high temporal resolution, chemistry-transport-models are needed, which calculate concentrations of specific pollutants in continuous time steps. Satellite observations can thus be used to improve model performances.&lt;/p&gt;&lt;p&gt;There are no direct satellite-measurements of fine particulate matter (PM2.5) but ground-level concentrations of PM2.5 can be derived from optical parameters such as aerosol optical depth (AOD). A wide range of methods for the determination of PM2.5 concentrations from AOD measurements has been developed so far, but it is still a big challenge. In this study a semi-empirical approach based on the physical relationships between meteorological and optical parameters was applied to determine a first-guess of ground-level PM2.5 concentrations for the year 2018 and the larger Germany region. Therefor AOD observations of MODIS (Moderate Resolution Imaging Spectroradiometer) aboard the NASA Aqua satellite were used in a spatial resolution of 3km. First results showed an overestimation of ground-level aerosols and quiet low correlations with in-situ station measurements from the European Environmental Agency (EEA). To improve the results, correction factors were calculated using the coefficients of linear regression between satellite-based and in-situ measured particulate matter concentrations. Spatial and seasonal dependencies were taken into account with it. Correlations between satellite and in-situ measurements could be improved applying this method.&lt;/p&gt;&lt;p&gt;The MODIS 3km AOD product was found to be a good base for area-wide calculations of ground-level PM2.5 concentrations. First comparisons to the calculated PM2.5 concentrations from chemistry-transport-model POLYPHEMUS/DLR showed significant differences though. Satellite observations will now be used to improve the general model performance, first by helping to find and understand regional and temporal dependencies in the differences. As part of the German project S-VELD funded by the Federal Ministry of Transport and Digital Infrastructure BMVI, it will help for example to adjust the derivation of particle emissions within the model.&lt;/p&gt;

scholarly journals Understanding Spatial Variability of Air Quality in Sydney: Part 2—A Roadside Case Study

Atmosphere ◽  
2019 ◽  
Vol 10 (4) ◽  
pp. 217 ◽  
Author(s):  
Imogen Wadlow ◽  
Clare Paton-Walsh ◽  
Hugh Forehead ◽  
Pascal Perez ◽  
Mehrdad Amirghasemi ◽  
...  
Keyword(s):  
Air Quality ◽  
Fine Particulate Matter ◽  
Temporal Variations ◽  
Subject Area ◽  
Traffic Model ◽  
Urban Landscapes ◽  
Study Region ◽  
Main Road ◽  
Agent Based ◽  
Clean Air

Motivated by public interest, the Clean Air and Urban Landscapes (CAUL) hub deployed instrumentation to measure air quality at a roadside location in Sydney. The main aim was to compare concentrations of fine particulate matter (PM2.5) measured along a busy road section with ambient regional urban background levels, as measured at nearby regulatory air quality stations. The study also explored spatial and temporal variations in the observed PM2.5 concentrations. The chosen area was Randwick in Sydney, because it was also the subject area for an agent-based traffic model. Over a four-day campaign in February 2017, continuous measurements of PM2.5 were made along and around the main road. In addition, a traffic counting application was used to gather data for evaluation of the agent-based traffic model. The average hourly PM2.5 concentration was 13 µg/m3, which is approximately twice the concentrations at the nearby regulatory air quality network sites measured over the same period. Roadside concentrations of PM2.5 were about 50% higher in the morning rush-hour than the afternoon rush hour, and slightly lower (reductions of <30%) 50 m away from the main road, on cross-roads. The traffic model under-estimated vehicle numbers by about 4 fold, and failed to replicate the temporal variations in traffic flow, which we assume was due to an influx of traffic from outside the study region dominating traffic patterns. Our findings suggest that those working for long hours outdoors at busy roadside locations are at greater risk of suffering detrimental health effects associated with higher levels of exposure to PM2.5. Furthermore, the worse air quality in the morning rush hour means that, where possible, joggers and cyclists should avoid busy roads around these times.

Sign in / Sign up

Export Citation Format

Share Document