How Aclima collects and makes sense out of the world's most advanced hyperlocal

Aclima has introduced a new way to measure 

 and protect human health. Aclima provides hardware, software, high-quality scientific measurements, and insights that are backed by methods that have been rigorously tested, peer-reviewed, and validated by leading scientists at some of the world's top research institutions including the US

 to request further information about Aclima’s high-quality scientific measurements and patent-protected methodologies.

. Once in the field, Aclima’s vehicle fleet collects 1-second measurements across each 330 feet (100 meters) of road, a 

, based on scientifically designed drive plans to ensure balance.

 installed on vehicle fleets, Aclima measures gases and particles in the air on each 

 process ensures the highest quality data.

 (AMN) are tuned based on measurements of known values; i.e., 

 each week during collection. Some data are flagged and excluded from further analysis.

Maps undergo extensive scientific review in order to enable insights that are based on ten years of research and development by Aclima’s world-class engineers and scientists.

Aclima's measurements are thoroughly evaluated during and after collection. The final dataset is then validated and 

Combining data points so you can view Aclima’s measured 

levels by county, and in the context of measurements recorded by nearby 

Air pollution affects billions of people worldwide, yet ambient pollution measurements are limited for much of the world. Urban air pollution concentrations vary sharply over short distances (≪1 km) owing to unevenly distributed emission sources, dilution, and physicochemical transformations. Accordingly, even where present, conventional fixed-site pollution monitoring methods lack the spatial resolution needed to characterize heterogeneous human exposures and localized pollution hotspots. Here, we demonstrate a measurement approach to reveal urban air pollution patterns at 4–5 orders of magnitude greater spatial precision than possible with current central-site ambient monitoring. We equipped Google Street View vehicles with a fast-response pollution measurement platform and repeatedly sampled every street in a 30-km2 area of Oakland, CA, developing the largest urban air quality data set of its type. Resulting maps of annual daytime NO, NO2, and black carbon at 30 m-scale reveal stable, persistent pollution patterns with surprisingly sharp small-scale variability attributable to local sources, up to 5–8× within individual city blocks. Since local variation in air quality profoundly impacts public health and environmental equity, our results have important implications for how air pollution is measured and managed. If validated elsewhere, this readily scalable measurement approach could address major air quality data gaps worldwide.

High-Resolution Air Pollution Mapping with Google Street View Cars: Exploiting Big Data | Environmental Science & Technology

https://pubs.acs.org/doi/full/10.1021/acs.est.7b00891

Air pollution measurements collected through systematic mobile monitoring campaigns can provide outdoor concentration data at high spatial resolution. We explore approaches to minimize data requirements for mapping a city’s air quality using mobile monitors with “data-only” versus predictive modeling approaches. We equipped two Google Street View cars with 1-Hz instruments to collect nitric oxide (NO) and black carbon (BC) measurements in Oakland, CA. We explore two strategies for efficiently mapping spatial air quality patterns through Monte Carlo analyses. First, we explore a “data-only” approach where we attempt to minimize the number of repeated visits needed to reliably estimate concentrations for all roads. Second, we combine our data with a land use regression-kriging (LUR-K) model to predict at unobserved locations; here, measurements from only a subset of roads or repeat visits are considered. Although LUR-K models did not capture the full variability of on-road concentrations, models trained with minimal data consistently captured important covariates and general spatial air pollution trends, with cross-validation R2 for log-transformed NO and BC of 0.65 and 0.43. Data-only mapping performed poorly with few (1–2) repeated drives but obtained better cross-validation R2 than the LUR-K approach within 4 to 8 repeated drive days per road segment.

Mapping Air Pollution with Google Street View Cars: Efficient Approaches with Mobile Monitoring and Land Use Regression | Environmental Science & Technology

https://pubs.acs.org/doi/full/10.1021/acs.est.8b03395

Mobile-platform measurements provide new opportunities for characterizing spatial variations in air pollution within urban areas, identifying emission sources, and enhancing knowledge of atmospheric processes. The Aclima, Inc., mobile measurement and data acquisition platform was used to equip four Google Street View cars with research-grade instruments, two of which were available for the duration of this study.

Mobile-platform measurement of air pollutant concentrations in California: performance assessment, statistical methods for evaluating spatial variations, and spatial representativeness

https://amt.copernicus.org/articles/13/3277/2020/

It is known, to researchers and heavily impacted communities, that people of color face a higher average burden of air pollution. It was unknown whether racial/ethnic disparities were caused by spatial heterogeneities at the level of city ...Disparity in air pollution exposure arises from variation at multiple spatial scales: along urban-to-rural gradients, between individual cities within a metropolitan region, within individual neighborhoods, and between city blocks. Here, we improve on ...

Local- and regional-scale racial and ethnic disparities in air pollution determined by long-term mobile monitoring

https://www.pnas.org/doi/abs/10.1073/pnas.2109249118

This guest post is by Smridh Malhotra. Hexagons are a popular, useful way to view your data in thematic maps. They’re especially helpful when you want to look for patterns or clusters in large amounts of point data.

Use Hexagons to Visualize Your Map Data with H3 Aggregations

https://blogs.oracle.com/oraclespatial/post/use-hexagons-to-visualize-your-map-data-with-h3-aggregations

The Future of Air Quality and Greenhouse Gas Measurement 

 around the clock for a one- year period in each county, beginning in October 2019 and ending in March 2021. For each county, the tool provides details about the 

were taken from all of Aclima’s measurements within the county during the measurement period.

Here you can see the counties where Aclima measured 

 in each county, beginning in October 2019 and ending in March 2021. The annual estimates of typical 

contain a greater number of samples, which improves the estimates relative to those at the 

-based estimates may also show data for locations that are not captured in the 

view due to insufficient data. However, in some cases, the 

-level estimate may not be a good representation of a singular address within the 

contains major traffic corridors and the other half is primarily residential, the 

estimate will not show these patterns. A similar situation can occur if certain parts of a 

You can view an area report by clicking on an individual 

from the map. Area reports show the individual 

 that Aclima measured and used to calculate the 

 and view the ambient concentrations within the context of WHO guidelines and US

Enter an address in the top left search box to see and share a custom 

 report for that location. It will include 

 type and concentration details for the nearest 

Aclima measured during the time period. If you don’t have a specific address you want to explore, you can click within a 