Update Initiatives “pwwb”

content/about/initiatives/pwwb.md

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 type = "about/initiatives"
-title = 'Partnership Project: Predicting What We Breathe'
-topics = ['API', 'Community']
-featured_image = '/uploads/city_of_la_logo.png'
+title = "Partnership Project: Predicting What We Breathe"
+topics = ["API", "Community"]
+featured_image = "/uploads/city_of_la_logo.png"
 weight = 6
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+[Predicting What We Breathe](https://airquality.lacity.org/) (PWWB) and Predictive Environmental Analytics and Community Engagement for Equity and Environmental Justice (PEACE for EEJ) are NASA-funded partnership projects between the City of Los Angeles, California State University Los Angeles and OpenAQ.
 
-[Predicting What We Breathe](https://airquality.lacity.org/) is a partnership between the City of Los Angeles, California State University Los Angeles and OpenAQ. Funded by NASA, the project has developed a forecasting algorithm for air pollution based on time-series measurements of satellite and ground-level air quality data along with machine learning. The tool has reached an accuracy rate of 80-93% compared to ground data alone.
+PWWB has built a highly accurate open-source machine learning model (>94% accuracy for a 24-hour period) for L.A. County that provides hourly air quality predictions for six major pollutants at an extremely high spatial resolution of 250 m2. Using these predictions, local officials can now issue more timely and effective on-the-ground interventions and better understand the impact of their efforts to reduce air pollution. The PWWB model is an open-source tool available to jurisdictions worldwide to enhance pollution mitigation.
 
 {{< figure src="/uploads/predicting_aq.webp" title= "Researchers at the California State University in Los Angeles use machine learning to do air quality forecasting with high level of accuracy. Stock photo from lacity.org" >}}  
 
-The tool will inform policy and public health alerts and will be available on GitHub for use by communities worldwide. Forthcoming work includes integrating socioeconomic data so that the tool can better predict air pollution and its health effects on the most vulnerable communities–those exposed to higher levels of air pollution and its health consequences, which are typically environmental justice communities. OpenAQ assists the team with data aggregation and normalization as well as workshop development and execution.
+PEACE for EEJ is expanding the PWWB predictive model to incorporate socioeconomic, demographic and health data that will help the city support neighborhoods and communities that face higher exposures to air pollution and experience greater health impacts. The project is engaging with environmental justice organizations and organizations that represent minorities, immigrants, poor and elderly to understand their concerns and current use of data, and to co-create project tools, including a mobile app and a web application dashboard.
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+OpenAQ assists the team with data aggregation and normalization as well as workshop development and execution.