air quality

Human Aspects of Machine Learning – Inside Machine learning – Medium


As machine learning (ML) is being adopted more widely, human lives are being irreversibly transformed . Some ML uses are visible but many others are not even noticeable, already working behind the scenes. First, our daily lives are becoming more influenced by "machine-generated" insights. The web pages you browse seem to know the next questions you would like to ask. Restaurant recommendations are becoming sharper and routes to your destination are optimized based on up-to-date traffic and road conditions, even more optimized than drivers can intuit based on their experiences.

What Are Smart Cities (And Why Should We Care)?


You can be forgiven if your first reaction to hearing the term "smart cities" is an eye roll. Sure, we have smart diapers, smart toothbrushes and smart faucets, but cities? How is that even possible?

Microsoft shares pre-order details for the $319 Cortana thermostat


Microsoft unveiled its Cortana-powered thermostat, called GLAS, back in July, and now we have more details on it. The software giant partnered with Johnson Controls, the maker of the first in-room thermostat, to create the device, and it's a beauty. It also comes with a hefty price tag: $319, and is available for pre-order now for delivery in March 2018.

Foobot Home Air Quality Monitor review: A pricey way to keep an eye on indoor pollution


Contrary to what the name might indicate, Foobot is not a robot at all. On the contrary, it's a hunk of white plastic that sits on the shelf and does only one thing: Monitor the air in your house for pollutants.A product that's somewhat similar in design and function to the Netatmo Healthy Home Coach, the Foobot is an upright cylinder designed to be plugged in and left unattended. It does nothing on its own, but once you install Foobot's app, you're given a significant level of insight into your indoor air quality.

Rams make contingency plans in case air quality from fires affects ability to practice

Los Angeles Times

The Rams are making contingency plans if Southland fires continue to affect air quality that prevents them from practicing at their Thousand Oaks facility in preparation for Sunday's game against the Philadelphia Eagles at the Coliseum.

Recover Missing Sensor Data with Iterative Imputing Network Machine Learning

Sensor data has been playing an important role in machine learning tasks, complementary to the human-annotated data that is usually rather costly. However, due to systematic or accidental mis-operations, sensor data comes very often with a variety of missing values, resulting in considerable difficulties in the follow-up analysis and visualization. Previous work imputes the missing values by interpolating in the observational feature space, without consulting any latent (hidden) dynamics. In contrast, our model captures the latent complex temporal dynamics by summarizing each observation's context with a novel Iterative Imputing Network, thus significantly outperforms previous work on the benchmark Beijing air quality and meteorological dataset. Our model also yields consistent superiority over other methods in cases of different missing rates.

pg-Causality: Identifying Spatiotemporal Causal Pathways for Air Pollutants with Urban Big Data Artificial Intelligence

Many countries are suffering from severe air pollution. Understanding how different air pollutants accumulate and propagate is critical to making relevant public policies. In this paper, we use urban big data (air quality data and meteorological data) to identify the \emph{spatiotemporal (ST) causal pathways} for air pollutants. This problem is challenging because: (1) there are numerous noisy and low-pollution periods in the raw air quality data, which may lead to unreliable causality analysis, (2) for large-scale data in the ST space, the computational complexity of constructing a causal structure is very high, and (3) the \emph{ST causal pathways} are complex due to the interactions of multiple pollutants and the influence of environmental factors. Therefore, we present \emph{p-Causality}, a novel pattern-aided causality analysis approach that combines the strengths of \emph{pattern mining} and \emph{Bayesian learning} to efficiently and faithfully identify the \emph{ST causal pathways}. First, \emph{Pattern mining} helps suppress the noise by capturing frequent evolving patterns (FEPs) of each monitoring sensor, and greatly reduce the complexity by selecting the pattern-matched sensors as "causers". Then, \emph{Bayesian learning} carefully encodes the local and ST causal relations with a Gaussian Bayesian network (GBN)-based graphical model, which also integrates environmental influences to minimize biases in the final results. We evaluate our approach with three real-world data sets containing 982 air quality sensors, in three regions of China from 01-Jun-2013 to 19-Dec-2015. Results show that our approach outperforms the traditional causal structure learning methods in time efficiency, inference accuracy and interpretability.

Banking on Big Data -- Environmental Protection


Sophisticated tools capable of collecting and analyzing massive data sets and then displaying the results in visual form are no longer an option. They are becoming a necessity. On a daily basis, thousands upon thousands of monitoring stations around the world collect vast quantities of air quality data for use in spotting pollution problems, analyzing air quality trends, and guiding effective responses. To date, these monitoring stations have served as digital eyes and ears trained on the planet's atmosphere. But all of that seems likely to change in the not-too-distant future as evolving networks of air sensors that are just now beginning to be deployed around the globe result in an avalanche of data, all of which has the very real potential to overwhelm those trying to make sense of it.