Smokelyze

R shiny [05]: Interactive maps using Leaflet

Thu, 27 Mar 2025 00:00:00 +0000

Leaflet

The Leaflet package in R is a powerful tool for creating interactive web maps directly from R code, leveraging the popular open-source JavaScript library Leaflet.js (here). It allows users to overlay data points, polygons, raster images, and other geospatial data on customizable basemaps, supporting features like zooming, panning, popups, color scales, and layer controls. Leaflet integrates seamlessly with spatial objects in R (e.g., sf, sp, raster, terra), making it especially useful for visualizing spatial data in R Shiny apps, reports, and dashboards.

Smoke tool tutorial [04]: GAM previous - Layer map

Thu, 13 Mar 2025 00:00:00 +0000

This tutorial is for the “PM2.5 and O3 smoke tool” app, available at (https://westar.shinyapps.io/PMO3smokeTool/).

The [GAM previous] tab allows users to explore and understand previously developed GAMs for O3 prediction and related air quality analyses. It includes two main sub-tabs: [Overview] and [Layer map]. Additionally, users can compare not only our GAM results but also findings from other studies on smoke contributions to O3 and PM2.5, derived using different methods.

Smoke tool tutorial [03]: GAM previous - Overview

Wed, 12 Mar 2025 00:00:00 +0000

This tutorial is for the “PM2.5 and O3 smoke tool” app, available at (https://westar.shinyapps.io/PMO3smokeTool/).

Smoke tool tutorial [02]: GAM manual - GAM setup

Tue, 11 Mar 2025 00:00:00 +0000

This tutorial is for the “PM2.5 and O3 smoke tool” app, available at (https://westar.shinyapps.io/PMO3smokeTool/).

The [GAM manual] tab is designed to provide users with comprehensive tools to manually manage data collection and loading processes for building GAMs. It includes two main sub-tabs: [Data collection], [Load dataset], [GAM setup], and [GAM results]. Please note that [GAM setup] and [GAM results] are normally hidden unless you run [Load datset].

Smoke tool tutorial [01]: GAM manual - Data collection

Mon, 10 Mar 2025 00:00:00 +0000

This tutorial is for the “PM2.5 and O3 smoke tool” app, available at (https://westar.shinyapps.io/PMO3smokeTool/).

R shiny [04]: Hide/Show login page

Mon, 10 Feb 2025 00:00:00 +0000

As explained in the previous post, creating a simulated login page in R Shiny is straightforward. This post demonstrates how to toggle the visibility of the login page, allowing a seamless transition from the login page to the main page. This content includes the necessary information for the server section. The main focus of this post is to explain the transition of the login page using actionButton(). The part where the user enters an email and proceeds will not be covered in this post.

R shiny [03]: Create login page

Fri, 07 Feb 2025 00:00:00 +0000

Implementing a typical login page can be quite complex in R shiny. Implementing such a typical login page requires handling user information and passwords, which presents limitations when building it in R Shiny. This post demonstrates how to create a “login page” in an R Shiny app. More precisely, it might be more appropriate to call it a simulated login page.

For example, you could create a feature that takes guests from the login page to the main UI page by simply entering their email address. Furthermore, you can even have your visitors receive a notification via email as soon as they enter their email address.

R shiny [02]: Dashboard UI

Wed, 05 Feb 2025 00:00:00 +0000

R shiny is a very useful and versatile tool to build your own app using R. This blog section will walk you through the step-by-step process of how the GAM tool app was built. Please note that your R environment and package versions may differ from mine. Today’s topic will cover how the basic UI was set up.

Install and import required pacakges

To begin, let’s set up a simple shiny application with a basic UI and server structure. Before creating the app, ensure you have the necessary libraries installed. If not, you can install them using:

R shiny [01]: Getting started

Tue, 04 Feb 2025 00:00:00 +0000

R Shiny

R Shiny is an open-source R package that allows users to build interactive web applications directly in R without requiring extensive knowledge of web development (HTML, CSS, or JavaScript). It is widely used for data visualization, dashboard creation, and interactive analytics. Shiny applications run in a web browser and can be hosted locally, on Shiny Server, or deployed to ShinyApps.io or other cloud platforms. R Shiny is a game-changer for those who want to create interactive applications and data-driven dashboards without needing deep web development expertise. Whether you’re an analyst, researcher, or developer, Shiny provides a powerful and flexible way to bring your data to life.

Mon, 01 Jan 0001 00:00:00 +0000

Haebum Lee, PhD.
(Developer)

Postdoctoral Scholar

Jaffe Group,
School of STEM,
University of Washington (UW),
Bothell, WA, United States

Professional Summary

I am an Atmospheric and Data Scientist (Ph.D.) with 8+ years of experience blending atmospheric air quality modeling, machine/deep learning, and cloud-scale geospatial data analysis. I have a proven track record of bridging the gap between rigorous academic research and production-ready intelligence tools, synthesizing multi-hazard environmental parameters to drive data-informed decisions.

Mon, 01 Jan 0001 00:00:00 +0000

Contact Us

Have any questions or feedback?
Fill out the form below and we will get back to you as soon as possible.

Mon, 01 Jan 0001 00:00:00 +0000

Privacy Policy

Last Updated: February 25, 2026

This Privacy Policy explains how Smokelyze ("we," "us," or "our") collects, uses, and discloses information about you when you access or use our web application. By using Smokelyze, you agree to the collection and use of information in accordance with this policy.

1. Information We Collect

We only collect information that is necessary to provide and improve our services.

Mon, 01 Jan 0001 00:00:00 +0000

Smokelyze

[Smokelyze], formerly known as [PMO3smokeTool],
is a comprehensive analytical platform for wildfire smoke impact assessment.

Smokelyze Map (JavaScript version)
PMO3smokeTool (Legacy: R shiny version)

Purpose of tool

The purpose of this app is to demonstrate the application of a Generalized Additive Model (GAM) for predicting MDA8 O3 using meteorological data under both smoke and non-smoke conditions. In this app, a GAM is derived using the "mgcv" package in R. We hope this tool will be useful for state agencies and others in understanding the factors that control O3 production.

Mon, 01 Jan 0001 00:00:00 +0000

Terms of Use & Disclaimer

Last Updated: March 3, 2026

Welcome to Smokelyze. By accessing or using this application, you agree to be bound by these Terms of Use. If you do not agree with any part of these terms, please do not use our services.

1. Educational and Informational Purposes Only

Smokelyze is an educational and research-oriented web application developed to provide advanced spatiotemporal analytics for wildfire smoke and air quality. The data, metrics, predictive models, and informational content ("Content") provided on this site are for general informational and research purposes only.

Mon, 01 Jan 0001 00:00:00 +0000

Layers

Map Type

Boundary Color/Width

NIFC

WF incident locations

AirNow

Obs MDA8 Obs PM2.5 Obs O3 (hourly) Obs PM2.5 (hourly) Obs NO2 (hourly)

Satellite & Model

HMS-smoke HMS-fire

TEMPO-NO2VCD (hourly) TEMPO-HCHOVCD (hourly)

TROPOMI-NO2VCD TROPOMI-HCHOVCD

HRRR-smokeVCD (hourly) HRRR-smoke8m (hourly)

GOES-AOD-East (hourly) GOES-AOD-West (hourly) GOES-GeoColor-East (hourly)

VIIRS-TrueColor

MODIS area burned

Published & Latest

Select dataset:

Obs MDA8 Pred MDA8 SMO Residual Quant residual

Obs PM2.5 Quant PM2.5 PM2.5-crit PM2.5-crit m0p5m PM2.5-crit m1p0m Smoke PM2.5 m0p5m Smoke PM2.5 m1p0m

TMAX SRAD

Pred MDA8 (EDM) SMO (EDM) Residual (EDM) Quant residual (EDM)

Smoke day (SMD) SMO > 97.5th SMO > 97.5th (EDM)

Smoke day (SMD) m0p5m Smoke day (SMD) m1p0m ExcDay ExcDay (EDM) ExcDay m0p5m ExcDay m1p0m

MapPost detail

Results are saved in your local browser (max 20 items).

No trajectories yet.
Click a point on map or use the button above.

Results are saved in your local browser (max 10 items).

No simulations yet.
Click a point on map or use the button above.

No area drawn yet.
Use the toolbar button below to draw.

Ai Chat (Beta)

Dispersion Animator

Step: 0 / 0

Current: ...

Mon, 01 Jan 0001 00:00:00 +0000

Announcements & Updates

Mon, 01 Jan 0001 00:00:00 +0000

Data Archive

UW GAM-v2 (Lee and Jaffe, 2025)

[Full dataset]: University of Washington ResearchWorks Archive, https://hdl.handle.net/1773/52893
[Citation]: Lee, H. and Jaffe, D. A.: Impact of Wildfires on O3 and Air Quality Across the United States for 2019–2024 Using Generalized Additive Models, J. Geophys. Res.: Atmos., 130, e2025JD044088, 2025. https://doi.org/10.1029/2025JD044088

UW GAM-v1 (Lee and Jaffe, 2024)

[Full dataset]: University of Washington ResearchWorks Archive, http://hdl.handle.net/1773/51276
[Citation]: Lee, H. and Jaffe, D. A.: Wildfire impacts on O3 in the continental United States using PM2.5 and a generalized additive model (2018–2023), Environ. Sci. Technol., 58, 14764–14774, 2024. https://doi.org/10.1021/acs.est.4c05870

UW Smoke PM2.5 (Jaffe et al., 2026)

[Full dataset]: University of Washington ResearchWorks Archive, https://hdl.handle.net/1773/53059
[Citation]: Jaffe, D., Lee, H., Magzamen, S., Goldberg, D., and O'Dell, K.: Health and Regulatory Impacts of PM2.5 from Wildland Fires for 2019–2024 in the US, GeoHealth, 10, e2025GH001576, 2026. https://doi.org/10.1029/2025GH001576

EPA EMBER (Simon et al., 2024)

[Full dataset]: Zenodo, https://zenodo.org/records/13737754
[Citation]: Simon, H. Beidler, J., Baker, K. R., Henderson, B. H., Fox, L., Misenis, C., Campbell, P., Vukovich, J., Possiel, N., and Eyth, A.: Expediated modeling of burn events results (EMBER): A screening-level dataset of 2023 ozone fire impacts in the US, Data in Brief, 58, 111208, 2024. https://doi.org/10.1016/j.dib.2024.111208
[Note]: Since this dataset has been reprocessed to fit our application (Smokelyze), for the original dataset, please see the detailed information: https://www.epa.gov/air-quality-analysis/expedited-modeling-burn-events-results-ember

Dataset

State

AQS

Query Results

Mon, 01 Jan 0001 00:00:00 +0000

Run your own GAMs

Professional interface for Generalized Additive Models. Build, configure, and execute R-based statistical models in real-time.

Step 1: Import Data

Select Source Dataset

This tool supports simple GAM simulations only. For large datasets (> 3000 rows), please perform the analysis directly in R.

Page 1 of 1

Step 2: GAM Building Pipeline

Quick Guide:

For those familiar with mgcv GAM, you can configure the model settings as follows:

Mon, 01 Jan 0001 00:00:00 +0000

Smokelyze

R shiny [05]: Interactive maps using Leaflet

Leaflet

Smoke tool tutorial [04]: GAM previous - Layer map

Smoke tool tutorial [03]: GAM previous - Overview

Smoke tool tutorial [02]: GAM manual - GAM setup

Smoke tool tutorial [01]: GAM manual - Data collection

R shiny [04]: Hide/Show login page

R shiny [03]: Create login page

R shiny [02]: Dashboard UI

Install and import required pacakges

R shiny [01]: Getting started

R Shiny

Haebum Lee, PhD. (Developer)

Postdoctoral Scholar

Professional Summary

Contact Us

Privacy Policy

1. Information We Collect

Smokelyze

[Smokelyze], formerly known as [PMO3smokeTool], is a comprehensive analytical platform for wildfire smoke impact assessment.

Purpose of tool

Terms of Use & Disclaimer

1. Educational and Informational Purposes Only

Layer Descriptions

Wildfire News

MapPost detail

HYSPLIT Simulation

Dispersion Screening

Legend

Area Statistics

Statistical Tools

New MapPost

HYSPLIT Simulation

Dispersion Parameters:

Dispersion Screening

Emission Data:

Meteorological Data:

Advanced Stack Data:

Chemistry Data (NOx):

MAKEMET Data:

Building Data:

Receptor Data:

Fumigation Data:

Terrain Output:

Time-Series Plot

Export Time-lapse

Smokelyze Usage Analytics

Announcements & Updates

New Announcement

Data Archive

UW GAM-v2 (Lee and Jaffe, 2025)

UW GAM-v1 (Lee and Jaffe, 2024)

UW Smoke PM2.5 (Jaffe et al., 2026)

EPA EMBER (Simon et al., 2024)

Query Results

Run your own GAMs

Step 1: Import Data

Step 2: GAM Building Pipeline

Quick Guide:

Links

For useful data archives

For spatial information

For wildfire information

For models

Haebum Lee, PhD.
(Developer)

[Smokelyze], formerly known as [PMO3smokeTool],
is a comprehensive analytical platform for wildfire smoke impact assessment.