Mapping urban air quality in near real-time using observations from low-cost sensors and model information

As the number of official monitoring stations for measuring urban air pollutants such as nitrogen oxides (NOx), particulate matter (PM) or ozone (O3) in most cities is quite small, it is difficult to determine the real human exposure to those pollutants. Therefore, several groups have established spatially higher resolved monitoring networks using low-cost sensors to create a finer concentration map [1-3].We are currently establishing a low-cost, but high-accuracy network in Munich to measure the concentrations of NOx, PM, O3, CO and additional environmental parameters. For that, we developed a compact stand-alone sensor systems that requires low power, automatically measures the respective parameters every minute and sends the data to our server. There the raw data is transferred into concentration values by applying the respective sensitivity function for each sensor. These functions are determined by calibration measurements prior to the distribution of the sensors.In contrast to the other existing networks, we will apply a recurring calibration method using a mobile high precision calibration unit (reference sensor) and machine learning algorithms. The results will be used to update the sensitivity function of each single sensor twice a week. &#160;With the help of this approach, we will be able to create a calibrated real-time concentration map of air pollutants in Munich.[1] Bigi et al.: Performance of NO, NO2 low cost sensors and three calibration approaches within a real world application, Atmos. Meas. Tech., 11, 3717&#8211;3735, 2018[2] Popoola et al., &#8220;Use of networks of low cost air quality sensors to quantify air quality in urban settings,&#8221; Atmos. Environ., 194, 58&#8211;70, 2018[3] Schneider et al.: Mapping urban air quality in near real-time using observations from low-cost sensors and model information, Environ. Int., 106, 234&#8211;247, 2017

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The Development and Application of Real-Time Monitoring and Forecasting System of Urban Air Quality

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.201-202.586 ◽

2012 ◽

Vol 201-202 ◽

pp. 586-589

Author(s):

Rui Lian Hou

Keyword(s):

Air Quality ◽

Real Time ◽

Urban Air Quality ◽

Urban Air ◽

Real Time Monitoring ◽

Network Database ◽

Low Energy Consumption ◽

Function Modules ◽

Monitoring And Forecasting ◽

Forecasting System

Underlying on the technologies of internet, network database and GIS, this paper presents the total solution of the development of the real-time monitoring and forecasting system model of urban air quality, which fulfils the requirements to low energy consumption and quick response and provides reference for similar project research.The paper systematically describes the system target,background of the development,running environment choice of the software, process of the development etc.Then it analyses function modules of the system.At last it gives the structures and implementation methods of the system’s database and the system security solution.This system not only can generate the state analysis reports and the early warning, but also can visualize the data analysing of the air quality by GIS.

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High-resolution mapping of urban air quality with heterogeneous observations: a new methodology and its application to Amsterdam

10.5194/amt-2019-410 ◽

2019 ◽

Author(s):

Bas Mijling

Keyword(s):

Air Pollution ◽

Air Quality ◽

Low Cost ◽

Urban Air Pollution ◽

Heterogeneous Data ◽

Urban Air Quality ◽

Quality Data ◽

Urban Air ◽

Reference Measurements ◽

Concentration Patterns

Abstract. In many cities around the world people are exposed to elevated levels of air pollution. Often local air quality is not well known due to the sparseness of official monitoring networks, or unrealistic assumptions being made in urban air quality models. Low-cost sensor technology, which has become available in recent years, has the potential to provide complementary information. Unfortunately, an integrated interpretation of urban air pollution based on different sources is not straightforward because of the localized nature of air pollution, and the large uncertainties associated with measurements of low-cost sensors. In this study, we present a practical approach to producing high spatio-temporal resolution maps of urban air pollution capable of assimilating air quality data from heterogeneous data streams. It offers a two-step solution: (1) building a versatile air quality model, driven by an open source atmospheric dispersion model and emission proxies from open data sources, and (2) a practical spatial interpolation scheme, capable of assimilating observations with different accuracies. The methodology, called Retina, has been applied and evaluated for nitrogen dioxide (NO2) in Amsterdam, the Netherlands, during the summer of 2016. The assimilation of reference measurements results in hourly maps with a typical accuracy of 39 % within 2 km of an observation location, and 53 % at larger distances. When low-cost measurements of the Urban AirQ campaign are included, the maps reveal more detailed concentration patterns in areas which are undersampled by the official network. During the summer holiday period, NO2 concentrations drop about 10 % due to reduced urban activity. The reduction is less in the historic city center, while strongest reductions are found around the access ways to the tunnel connecting the northern and the southern part of the city, which was closed for maintenance. The changing concentration patterns indicate how traffic flow is redirected to other main roads. Overall, we show that Retina can be applied for an enhanced understanding of reference measurements, and as a framework to integrate low-cost measurements next to reference measurements in order to get better localized information in urban areas.

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Networks of Air Quality Sensors and Their Use for High-resolution Mapping of Urban Air Quality

10.5194/egusphere-egu2020-9172 ◽

2020 ◽

Author(s):

Philipp Schneider ◽

Nuria Castell ◽

Paul Hamer ◽

Sam-Erik Walker ◽

Alena Bartonova

Keyword(s):

Sensor Networks ◽

Air Quality ◽

High Resolution ◽

Air Pollutants ◽

Low Cost ◽

Urban Air Quality ◽

Urban Air ◽

Sensor Systems ◽

Air Quality Model ◽

Quality Model

One of the most promising applications of low-cost sensor systems for air quality is the possibility to deploy them in relatively dense networks and to use this information for mapping urban air quality at unprecedented spatial detail. More and more such dense sensor networks are being set up worldwide, particularly for relatively inexpensive nephelometers that provide PM2.5 observations with often quite reasonable accuracy. However, air pollutants typically exhibit significant spatial variability in urban areas, so using data from sensor networks alone tends to result in maps with unrealistic spatial patterns, unless the network density is extremely high. One solution is to use the output from an air quality model as an a priori field and as such to use the combined knowledge of both model and sensor network to provide improved maps of urban air quality. Here we present our latest work on combining the observations from low-cost sensor systems with data from urban-scale air quality models, with the goal of providing realistic, high-resolution, and up-to-date maps of urban air quality.In previous years we have used a geostatistical approach for mapping air quality (Schneider et al., 2017), exploiting both low-cost sensors and model information. The system has now been upgraded to a data assimilation approach that integrates the observations from a heterogeneous sensor network into an urban-scale air quality model while considering the sensor-specific uncertainties. The approach further ensures that the spatial representativity of each observation is automatically derived as a combination of a model climatology and a function of distance. We demonstrate the methodology using examples from Oslo and other cities in Norway. Initial results indicate that the method is robust and provides realistic spatial patterns of air quality for the main air pollutants that were evaluated, even in areas where only limited observations are available. Conversely, the model output is constrained by the sensor data, thus adding value to both input datasets.While several challenging issues remain, modern air quality sensor systems have reached a maturity level at which some of them can provide an intra-sensor consistency and robustness that makes it feasible to use networks of such systems as a data source for mapping urban air quality at high spatial resolution. We present our current approach for mapping urban air quality with the help of low-cost sensor networks and demonstrate both that it can provide realistic results and that the uncertainty of each individual sensor system can be taken into account in a robust and meaningful manner.&#160;Schneider, P., Castell N., Vogt M., Dauge F. R., Lahoz W. A., and Bartonova A., 2017. Mapping urban air quality in near real-time using observations from low-cost sensors and model information. Environment international, 106, 234-247.

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