This repository contains data for the manuscript: "Unraveling a black box: An open-source methodology for the field calibration of small air quality sensors." This includes: Raw data from the low-cost prototype EarthSense Zephyrs, as well as raw data from reference instrumentation. SC stands for "Summer Campaign" and WC stands for "Winter Campaign", denoting the two different campaigns assessed in this study. Abstract The last two decades have seen substantial technological advances in the development of low-cost air pollution instruments using small sensors. While their use continues to spread across the field of atmospheric chemistry, challenges remain in ensuring data quality and comparability of calibration methods. This study introduces a seven-step methodology for the field calibration of low-cost sensors using reference instrumentation with user-friendly guidelines, open access code, and a discussion of common barriers to such an approach. The methodology has been developed and is applicable for gas-phase pollutants, such as for the measurement of nitrogen dioxide (NO2) or ozone (O3). A full example of the application of this methodology to a case study in an urban environment using both Multiple Linear Regression (MLR) and the Random Forest (RF) machine-learning technique is presented with relevant R code provided, including error estimation. In this case, we have applied it to the calibration of metal oxide gas-phase sensors (MOS). Results reiterate previous findings that MLR and RF are similarly accurate, though with differing limitations. The methodology presented here goes a step further than most studies by including explicit, transparent steps for addressing model selection, validation, and tuning, as well as addressing the common issues of autocorrelation and multicollinearity. We also highlight the need for standardized reporting of methods for data cleaning and flagging, model selection and tuning, and model metrics. In the absence of a standardized methodology for the calibration of low-cost sensors, we suggest a number of best practices for future studies using low-cost sensors to ensure greater comparability of research.

Ziel des BMBF-Programms Stadtklima im Wandel war die Entwicklung, Validierung und Anwendung eines gebaudeauflosenden Stadtklimamodells fur ganze Stadte. Das Verbundprojekt 3DO ubernahm die dem Modul B zugeordneten Forschungsaufgaben: Aufbereitung vorhandener Daten aus der Langzeitbeobachtung (LTO), Aufbau neuer Messstationen, Gewinnung neuer dreidimensionaler atmospharischer Daten und die Entwicklung neuer Konzepte z.B. zur Modellevaluation. Untersucht wurden der Aufbau der atmospharischen Grenzschicht, die Charakteristik der meteorologischen Parameter und deren Einfluss auf das thermische Empfinden des Menschen. Ein einheitlicher UC2-Datenstandard sowie Analysewerkzeuge wurden entwickelt und in ein Datenmanagementsystem und eine Wissensplattform fur den modulubergreifenden Austausch integriert.

One of the objectives of climatological part of project Young Cities ‘Developing Energy-Efficient Urban Fabric in the Tehran-Karaj Region’ is to simulate the micro climate (with 1m resolution) in 35ha of new town Hashtgerd, which is located 65 km far from mega city Tehran. The Project aims are developing, implementing and evaluating building and planning schemes and technologies which allow to plan and build sustainable, energy-efficient and climate sensible form mass housing settlements in arid and semi-arid regions (“energy-efficient fabric”). Climate sensitive form also means designing and planning for climate change and its related effects for Hashtgerd New Town. By configuration of buildings and open spaces according to solar radiation, wind and vegetation, climate sensitive urban form can create outdoor thermal comfort. To simulate the climate on small spatial scales, the micro climate model Envi-met has been used to simulate the micro climate in 35 ha. The Eulerian model ENVI-met is a micro-scale climate model which gives information about the influence of architecture and buildings as well as vegetation and green area on the micro climate up to 1 m resolution. Envi-met has been run with information from topography, downscaled climate data with neuro-fuzzy method, meteorological measurements, building height and different vegetation variants (low and high number of trees)