This document reports on a real-time air quality forecasting system that uses data-driven models to predict fine-grained air quality over the following 48 hours. The system uses a temporal predictor to model local air quality changes, a spatial predictor to model spatial correlations between locations, and an inflection predictor to handle sudden changes in air quality. An evaluation of the system in four Chinese cities found it could achieve 75% accuracy for the first 6 hours of predictions and 62% accuracy for the next 7-12 hours in Beijing, and it predicted sudden changes in air quality better than baseline methods.
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This is one of the presentations in the seminar.
More info in www.mmea.fi
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AERMOD Tiering Approach Case Study for 1-Hour NO2BREEZE Software
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Generating and Using Meteorological Data in AERMOD BREEZE Software
AERMOD, the preferred model of the U.S. EPA for near-field air dispersion modeling, requires the use of two meteorological files: the surface (.SFC) and profile (.PFL) files.
China testbed FMI-Enfuser in Langfang by Adj. Prof. Ari KarppinenCLEEN_Ltd
CLEEN's MMEA program organised an international seminar on cleaner air - Outdoor and indoor air quality together with Zhejiang University and assistant organizer Insigma group.
This is one of the presentations in the seminar.
More info in www.mmea.fi
The cleantech field is expanding rapidly and Finnish companies are committed to working for a better environment in the fields of energy efficiency, air quality and monitoring. The world-class Cleantech know-how from Finland and the cooperation with Chinese partners and the results were highlighted in the MMEA seminar. Some of the leading Finnish cleantech companies together with Finnish and Chinese research institutions were present at the event. The seminars focused on cooperation between Finland and China concerning indoor and outdoor air quality and solutions to make them better.
Sensitivity of AERMOD to AERMINUTE-Generated MeteorologyBREEZE Software
This study presents a comparison of the pollutant concentration predictions from the AERMOD and ISC air dispersion models in the context of fugitive storage tank emissions at a bulk petroleum storage terminal.
Lina Presented our CCNY work on the improvement of PM2.5 estimates for the NY state using satellite remote sensing Aerosol Optical Depth and meteorological information.
AERMOD Tiering Approach Case Study for 1-Hour NO2BREEZE Software
This study reviews 1-hour NO2 concentrations predicted by AERMOD for a hypothetical source at four locations throughout the United States with hourly varying background ozone concentrations.
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DSD-INT 2020 Radar rainfall estimation and nowcastingDeltares
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Air quality monitors were installed in the University area of Stuttgart, and some analyses were made with the collected data.
The current weather data is already online and can be found at
http://lhg-503.iws.uni-stuttgart.de/html/meteo/
El 29 de febrero y el 1 de marzo de 2016, la Fundación Ramón Areces analizó la relación entre 'Big Data y el cambio climático' en unas jornadas. ¿Puede el Big Data ayudar a reducir el cambio climático? ¿Cómo contribuirá ese análisis masivo de datos a prevenir y gestionar catástrofes naturales? Son solo algunas de las preguntas a las que intentarán responder los ponentes. Las ciencias vinculadas al clima tienen en el Big Data una herramienta muy prometedora para afrontar diferentes fenómenos asociados al cambio climático.
The PuffR R Package for Conducting Air Quality Dispersion AnalysesRichard Iannone
PuffR is all about helping you conduct dispersion modelling using the CALPUFF modelling system. It is a software package currently being developed using the R statistical programming language. Dispersion modelling is a great tool for understanding how pollutants disperse from sources to receptors, and, how these dispersed pollutants affect populations’ exposure. The presentation goes over basic concepts in air dispersion modelling using CALPUFF, the goals of the project are outlined, the PuffR workflow is described, and a project roadmap is provided.
Methane Maps of DISH and Flower Mound (Texas) - Likely Indication of Benzene ...Picarro
This map shows results of drive-by emissions sampling in DISH, Texas and neighboring Flower Mound as well as other parts of the Dallas-Fort Worth Area. The large plumes are possible indications of emissions of toxic VOCs from natural gas compression and storage facilities.
DSD-INT 2020 Radar rainfall estimation and nowcastingDeltares
Presentation by Ruben Imhoff, Xiaohan Li, Pieter Hazenberg, Deltares, at the Delft-FEWS International User Days 2020, during Delft Software Days - Edition 2020. Monday, 2 November 2020.
Air quality monitors were installed in the University area of Stuttgart, and some analyses were made with the collected data.
The current weather data is already online and can be found at
http://lhg-503.iws.uni-stuttgart.de/html/meteo/
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Available for download at http://solarreference.com/solar-resource-assessment-how-to-get-bankable-meteo-data/
This presentation from DLR (German Aerospace Center) explains.
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This presentation can also be downloaded at NREL
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Forecasting fine grained air quality based on big data
1. Forecasting Fine-Grained Air Quality
Based on Big Data
Date: 2015/10/15
Author: Yu Zheng, Xiuwen Yi, Ming Li1, Ruiyuan Li1, Zhangqing
Shan, Eric Chang, Tianrui Li
Source: KDD '15
Advisor: Jia-ling Koh
Spearker: LIN,CI-JIE
1
3. Introduction
People are increasingly concerned with air pollution, which impacts human
health and sustainable development around the world
There is a rising demand for the prediction of future air quality, which can
inform people’s decision making
3
4. Challenges
Multiple complex factors vs. insufficient and inaccurate data
Urban air changes over location and time significantly
Inflection points and sudden changes
Good [0-50) Moderate [50-100) Unhealthy [150-200)
Very Unhealthy [200-300)Unhealthy for sensitive [100-150)
A) Monitoring stations B) Distribution of the max-min gaps
C) AQI of different stations changing over time of day
Inflection Points
5. Introduction
Goal: construct a real-time air quality forecasting system that
uses data-driven models to predict fine-grained air quality over
the following 48 hours(first 6, 7-12, 12-24, and 24-48 hours)
5
10. Temporal Predictor (TP)
Considering the prediction more from its own historical and future
conditions (local)
A linear regression is employed to model the local change of air quality
Train a model respectively for each hour in the next six hours, and two
models for each time interval (from 7 to 48 hours) to predict its maximum
and minimum values
10
tc-1 tctc-2tc-h+1 tc+1 tc+6tc+2 tc+7 tc+12 tc+24 tc+48tc+13 tc+25
11. Features
The AQIs of the past ℎ hours at the station
The local meteorology (such as sunny, overcast, cloudy, foggy, humidity,
wind speed, and direction) at the current time 𝑡 𝑐
Time of day and day of the week
The weather forecasts (including Sunny/overcast/cloudy, wind speed, and
wind direction) of the time interval we are going to predict
11
13. Spatial Predictor (SP)
Modeling the spatial correlation of air pollution
Predicting the air quality from other locations’ status consisting of AQIs
and meteorological data
Train multiple spatial predictors corresponding to different future time
intervals
Two major steps:
Spatial partition and aggregation
Prediction based on a Neural Network
14. Spatial partition and aggregation
Partition the spatial space into regions by using three circles with different
diameters
Calculate the average AQI for a given kind of air pollutant; same for
temperature and humidity
Each region will only have one set of aggregated air quality readings and
meteorology
14
A) Spatial partition B) Spatial aggregation
S
15. Spatial Predictor
15
Features of SP
the AQI of the past three hours (𝑨𝑸𝑰𝑖)
meteorological features (𝑀 𝑖), including the wind speed and direction,
of the current time 𝑡 𝑐.
17. Prediction Aggregator(PA)
The prediction aggregator dynamically integrates the predictions that the
spatial and temporal predictors have made for a location
Feature Set
wind speed, direction, humidity, sunny, cloudy, overcast, and foggy
the predictions generated by the spatial and temporal predictors
the corresponding Δ𝐴𝑄𝐼 (from the ground truth)
Train a Regression Tree (RT) to model the dynamic combination of these
factors and predictions
17
20. Inflection Predictor
The air quality of a location changes sharply in a few hours
Too infrequent to be predicted
Invoke to handle sudden changes
Need to know when to invoke the IP model
20
Good [0-50) Moderate [50-100) Unhealthy [150-200)
Very Unhealthy [200-300)Unhealthy for sensitive [100-150)
A) Monitoring stations B) Distribution of the max-min gaps
C) AQI of different stations changing over time of day
Inflection Points
21. Inflection Predictor
1. Select the sudden drop instances 𝐷𝑖 from historical data 𝐷
AQI is bigger than 200 and decreases over a threshold in the next few hours
2. Find surpassing ranges and categories
21
D Di
Dt
PDF
PDF
c1 c2 c3 c4
a1 a2 a4a3
A) Select sudden
drop instances Di
B) Distributions of a
continuous feature
Di D-Di Di D-Di
C) Distributions of
a discrete feature
22. D Di
Dt
Inflection Predictor (IP)
𝐸 = 𝑀𝑎𝑥 (
|𝑥1|
𝐷𝑖
−
|𝑥2|
𝐷 − 𝐷𝑖
) ×
∆|𝑥1|
∆|𝑥2|
𝐷𝑡 = 𝑥1 ∪ 𝑥2 is a collection of instances retrieved by a set of surpassing ranges and categories
𝑥1
𝑥2
3. Select surpassing ranges and categories as thresholds
there are multiple surpassing ranges and categories, some of them may not
really be discriminative enough
need to find a set of surpassing ranges and categories as thresholds, with which
we can retrieve as many instances from 𝐷𝑖 as possible while involving the
instances from 𝐷− 𝐷𝑖 as few as possible
The problem can be solved by using Simulated Annealing
24. Inflection Predictor (IP)
4. Train an inflection predictor with 𝐷𝑡
The features used in the inflection predictor to determine the specific
drop values are the same as those of the temporal predictor
The inflection predictor is based on a RT
The output of the inflection predictor is a delta of AQI to be appended
to the final result
24
31. Conclusion
Report on a real-time air quality forecasting system that uses data-driven
models to predict fine-grained air quality over the following 48 hours
It can achieve an accuracy of 0.75 for the first 6 hours and 0.6 for the next
7-12 hours in Beijing
It predicts the sudden changes of air quality much better than baseline
methods
31