diff --git a/.gitignore b/.gitignore
index bf41ddf..bf6283c 100644
--- a/.gitignore
+++ b/.gitignore
@@ -6,3 +6,5 @@
 #exclude API keys
 api_keys/*
 
+#exclude data
+data/*
diff --git a/cities.csv b/cities.csv
index 776e14a..71a8a24 100644
--- a/cities.csv
+++ b/cities.csv
@@ -1,8 +1,8 @@
-City,Type,State,Country
-Madison,city,WI,United States
-Bellingham,city,WA,United States
-Portland,city,OR,United States
-Port Angeles,city,WA,United States
-Boston,city,MA,United States
-Boise City,city,ID,United States
-Salt Lake City,city,UT,United States
+City,Type,State,Country,CBSA-Code
+Madison,city,WI,United States,31540
+Bellingham,city,WA,United States,13380
+Portland,city,OR,United States,38900
+Port Angeles,city,WA,United States,38820
+Boston,city,MA,United States,14460
+Boise City,city,ID,United States,14260
+Salt Lake City,city,UT,United States,41620
diff --git a/city-compare.Rproj b/city-compare.Rproj
index 8e3c2eb..be80382 100644
--- a/city-compare.Rproj
+++ b/city-compare.Rproj
@@ -11,3 +11,5 @@ Encoding: UTF-8
 
 RnwWeave: Sweave
 LaTeX: pdfLaTeX
+
+BuildType: Makefile
diff --git a/city_compare.Rmd b/city_compare.Rmd
index a42ae6d..02dd6a3 100644
--- a/city_compare.Rmd
+++ b/city_compare.Rmd
@@ -36,7 +36,7 @@ census_api_key(key = substr(read_file(file = "api_keys/census_api_key"), 1, 40))
 
 ## Date ranges
 ```{r date_range, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
-date_start <- "2010-01-01"
+date_start <- "2014-01-01"
 date_end <- "2024-12-31"
 
 ```
@@ -44,42 +44,42 @@ date_end <- "2024-12-31"
 
 ```{r cities, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
 cities <- read_csv(file = "cities.csv")
-cities <- cities %>%
+cities <- cities |>
   mutate(city_name = paste0(City, " ", Type))
 
 ```
 
-# Get data
+# Data
 
 ## Census data
 ```{r census, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
 populations <- list(NULL)
 for(city in cities$city_name){
-  state <- cities %>% filter(city_name == city) %>% pull(State)
+  state <- cities |> filter(city_name == city) |> pull(State)
   populations[[city]] <- get_acs(
     geography = "place",
     variables = "B01003_001",
     state = state,
     year = 2023,
     geometry = TRUE
-  ) %>%
+  ) |>
   filter(str_detect(NAME, city))
 }
 
 populations <- bind_rows(populations)
 
-city_center <- populations %>%
-  st_centroid() %>%
-  st_transform(4326) %>%  # Convert to WGS84 (standard lat/lon)
+city_center <- populations |>
+  st_centroid() |>
+  st_transform(4326) %>%
   mutate(
     lon = st_coordinates(.)[,1],
     lat = st_coordinates(.)[,2]
-  ) %>%
-  st_drop_geometry() %>%
+  ) |>
+  st_drop_geometry() |>
   select(lat, lon)
 
 cities <- bind_cols(cities, populations, city_center)
-cities <- cities %>% mutate(
+cities <- cities |> mutate(
   density = estimate/as.double(st_area(geometry))*1000000
 )
 
@@ -120,12 +120,12 @@ ggplot(data = cities) +
 ```{r weather, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
 weather <- list(NULL)
 for(city in cities$City){
-  city_info <- cities %>% filter(City == city)
+  city_info <- cities |> filter(City == city)
   city_run <- weather_history(
-    location = c(city_info %>% pull(lat), city_info %>% pull(lon)),
+    location = c(city_info |> pull(lat), city_info |> pull(lon)),
     start = date_start,
     end = date_end,
-    daily = c("apparent_temperature_max", "apparent_temperature_min"),
+    daily = c("apparent_temperature_max", "apparent_temperature_min", "precipitation_hours"),
     response_units = list(temperature_unit = "fahrenheit")
   )
   city_run$city <- city
@@ -134,23 +134,27 @@ for(city in cities$City){
 
 weather <- bind_rows(weather)
 
-weather_summary <- weather %>%
+weather |>
   mutate(year = year(ymd(date)),
-         month = month(ymd(date))) %>%
-  group_by(year, city) %>%
-  summarise(days_above_80 = sum(daily_apparent_temperature_max > 80)) %>%
-  group_by(city) %>%
-  summarise(median_days_above_80 = median(days_above_80))
-
-ggplot(data = weather_summary) +
+         month = month(ymd(date))) |>
+  group_by(year, city) |>
+  summarise(days_above_80 = sum(daily_apparent_temperature_max > 80)) |>
+  group_by(city) |>
+  summarise(median_days_above_80 = median(days_above_80)) |>
+  ggplot() +
   geom_col(aes(x = city,
                y = median_days_above_80)) +
-  labs(title = "Days above 80°F",
+  labs(title = "Days above 80°F (apparent temperature)",
        y = "Median days per year",
        x = "City",
        fill = NULL)
 
-ggplot(data = weather %>% pivot_longer(cols = starts_with("daily"), names_to = "max_min", values_to = "temperature") %>% filter(max_min %in% c("daily_apparent_temperature_min", "daily_apparent_temperature_max"))) +
+weather |> 
+  pivot_longer(cols = starts_with("daily"), 
+               names_to = "max_min", 
+               values_to = "temperature") |> 
+  filter(max_min %in% c("daily_apparent_temperature_min", "daily_apparent_temperature_max")) |>
+ggplot() +
   geom_violin(aes(x = city,
                   y = temperature,
                   fill = max_min)) +
@@ -160,5 +164,167 @@ ggplot(data = weather %>% pivot_longer(cols = starts_with("daily"), names_to = "
        y = "°F",
        x = "City",
        fill = NULL)
+
+weather |>
+  mutate(year = year(ymd(date)),
+         month = month(ymd(date))) |>
+  group_by(year, city) |>
+  summarise(days_above_4hr = sum(daily_precipitation_hours > 4)) |>
+  group_by(city) |>
+  summarise(median_days_above_4hr = median(days_above_4hr)) |>
+  ggplot() +
+  geom_col(aes(x = city,
+               y = median_days_above_4hr)) +
+  labs(title = "Days with more than 4 hrs of rain",
+       y = "Median days per year",
+       x = "City",
+       fill = NULL)
+
+weather |>
+  mutate(year = year(ymd(date)),
+         month = month(ymd(date))) |>
+  group_by(year, month, city) |>
+  summarise(days_above_4hr = sum(daily_precipitation_hours > 4)) |>
+  group_by(city, month) |>
+  summarise(median_days_above_4hr = median(days_above_4hr)) |>
+  ggplot() +
+  geom_line(aes(x = month,
+               y = median_days_above_4hr,
+               color = city)) +
+  labs(title = "Days with more than 4 hrs of rain",
+       y = "Median days per month",
+       x = "Month",
+       color = "City")
+
 ```
 
+
+## Air Quality
+```{r air_quality, eval = TRUE, echo = TRUE, results = "show", warning = FALSE, error = TRUE, message = FALSE}
+# download data
+# Create data directory if it doesn't exist
+if (!dir.exists("data")) {
+  dir.create("data")
+}
+
+# Define years
+years <- seq(year(ymd(date_start)), year(ymd(date_end)), 1)
+
+# Initialize empty list to store dataframes
+aqi_list <- list()
+
+# Download and process files for each year
+for (year in years) {
+  # Construct URL
+  url <- paste0("https://aqs.epa.gov/aqsweb/airdata/daily_aqi_by_cbsa_", year, ".zip")
+  
+  # Define local file paths
+  zip_file <- file.path("data", paste0("daily_aqi_by_cbsa_", year, ".zip"))
+  csv_file <- file.path("data", paste0("daily_aqi_by_cbsa_", year, ".csv"))
+  
+  # Download zip if it doesn't exist
+  if (file.exists(zip_file)) {
+    cat(paste0("Zip file for year ", year, " already exists. Skipping download.\n"))
+  } else {
+    cat(paste0("Downloading data for year ", year, "...\n"))
+    
+    tryCatch({
+      download.file(url, zip_file, mode = "wb")
+      cat(paste0("Successfully downloaded data for year ", year, "\n"))
+    }, error = function(e) {
+      cat(paste0("Error downloading data for year ", year, ": ", e$message, "\n"))
+      next
+    })
+  }
+  
+  # Extract zip if CSV doesn't exist
+  if (file.exists(zip_file)) {
+    if (file.exists(csv_file)) {
+      cat(paste0("CSV for year ", year, " already extracted. Skipping extraction.\n"))
+    } else {
+      cat(paste0("Extracting zip for year ", year, "...\n"))
+      tryCatch({
+        unzip(zip_file, exdir = "data")
+        cat(paste0("Successfully extracted data for year ", year, "\n"))
+      }, error = function(e) {
+        cat(paste0("Error extracting data for year ", year, ": ", e$message, "\n"))
+        next
+      })
+    }
+  }
+  
+  # Read CSV if it exists
+  if (file.exists(csv_file)) {
+    cat(paste0("Reading CSV for year ", year, "...\n"))
+    tryCatch({
+      aqi_year <- read.csv(csv_file)
+      aqi_list[[as.character(year)]] <- aqi_year
+      cat(paste0("Successfully read ", nrow(aqi_year), " rows for year ", year, "\n"))
+    }, error = function(e) {
+      cat(paste0("Error reading CSV for year ", year, ": ", e$message, "\n"))
+    })
+  }
+}
+
+# Combine all dataframes
+aqi_data <- bind_rows(aqi_list)
+aqi_data <- aqi_data |>
+  filter(CBSA.Code %in% (cities |> pull(`CBSA-Code`))) |>
+  mutate(year = year(ymd(Date)),
+         month = month(ymd(Date)))
+
+aqi_threshhold <- 50
+
+aqi_data |>
+  group_by(year, CBSA) |>
+  summarise(hours_above = sum(AQI >= aqi_threshhold, na.rm = TRUE)) |>
+  group_by(CBSA) |>
+  summarise(mean_hours_above = mean(hours_above, na.rm = TRUE)) |>
+  ggplot() +
+  geom_col(aes(x = CBSA,
+               y = mean_hours_above)) +
+  labs(title = "Hours above 50 AQI",
+       y = "Mean hours per year",
+       x = "City",
+       fill = NULL)
+
+aqi_data |>
+  group_by(year, CBSA) |>
+  summarise(hours_above = sum(AQI >= 2*aqi_threshhold, na.rm = TRUE)) |>
+  group_by(CBSA) |>
+  summarise(mean_hours_above = mean(hours_above, na.rm = TRUE)) |>
+  ggplot() +
+  geom_col(aes(x = CBSA,
+               y = mean_hours_above)) +
+  labs(title = paste0("Hours above ", aqi_threshhold * 2," AQI"),
+       y = "Mean hours per year",
+       x = "City",
+       fill = NULL)
+
+aqi_data |> 
+  ggplot() +
+  geom_violin(aes(x = CBSA,
+                  y = AQI)) +
+  labs(title = "AQI",
+       y = "AQI",
+       x = "City",
+       fill = NULL) +
+  coord_cartesian(ylim = (c(0,500)))
+
+aqi_data |>
+  group_by(year, month, CBSA) |>
+  summarise(hours_above = sum(AQI >= aqi_threshhold, na.rm = TRUE)) |>
+  group_by(CBSA, month) |>
+  summarise(mean_hours_above = mean(hours_above, na.rm = TRUE)) |>
+  ggplot() +
+  geom_line(aes(x = month,
+               y = mean_hours_above,
+               color = CBSA)) +
+  scale_x_continuous(breaks = seq(1,12,1)) +
+  scale_color_brewer(type = "qual") +
+  labs(title = paste0("Hours with an AQI of greater than or equal to ", aqi_threshhold),
+       y = "Mean days per month",
+       x = "Month",
+       color = "City")
+
+```