Pollution data from RAMA

Note

In this script, I wrangle the pollution data. I downloaded it from aire.cdmx.gob.mx in EXCEL format. The updated data was sent to me by email because their site went down. The source that measures at the station by hour level for Mexico City is RAMA (Red Automática de Monitoreo Atmosférico) for CO, NO, NO2, NOX, 03, PM10, PM25, PMCO, SO2.

Warning

Warning: Script is heavy, relies on parallelization, takes long to run.

Some parameters: - I remove a measured day if more than 4 hours out of 24 are missing - Inverse distance weight within 20km

1 Import RAMA hourly data

First, list all the excel files.

all_files <-
  # get full path
  tibble(path = list.files(here("..", "raw_data", "3_aire_cdmx", "EXCEL_new"),
                           pattern = ".*xls$",
                           recursive = T,
                           full.names = T)) %>% 
  mutate(basename = tools::file_path_sans_ext(basename(path)),
         year_file = as.numeric(str_sub(basename, 1, 4)),
         variable = str_sub(basename, 5),
         source = str_extract(path, "(?<=EXCEL_new/)[^/]+")) %>% 
  filter(source == "RAMA")

Import all the data and join into a single dataframe.

# import all the data in lists
hourly_data <- 
  map(all_files %>% pull(variable) %>% unique(), ~{
    print(paste0("Importing: ", .x))
    
    all_files %>%
      filter(variable == .x) %>%
      pull(path) %>%
      map(~ read_xls(.)) %>% 
      list_rbind() %>% 
      pivot_longer(cols = -c(FECHA, HORA),
                   names_to = "station",
                   values_to = .x)
  }, .progress = TRUE)
[1] "Importing: CO"
[1] "Importing: NO"
[1] "Importing: NO2"
[1] "Importing: NOX"
[1] "Importing: O3"
[1] "Importing: PM10"
[1] "Importing: PM25"
[1] "Importing: PMCO"
[1] "Importing: SO2"
#reduce the lists by dataset
hourly_data <- hourly_data %>%
  reduce(full_join, by = c("FECHA", "HORA", "station"))

#rename and encode
hourly_data <- hourly_data %>%
  rename_with(tolower) %>%
  mutate(fecha = as_date(fecha))

rm(all_files)

2 Distances between stations and AGEBs

Location of all the stations

catalogo_est <- 
  read_csv(here("..",
                "raw_data",
                "3_aire_cdmx",
                "cat_estacion.csv"),
           skip = 1,
           locale = readr::locale(encoding = "latin1")) %>% 
  rename(station = cve_estac)

# all the stations are in the catalog
catalogo_est <- catalogo_est %>%
  filter(station %in% (hourly_data %>% pull(station) %>% unique()))

Locations of the AGEB

catalogo_ageb <-  
  st_read(here("..",
                "output",
                "ageb_poligonos.geojson")) %>% 
  st_transform(4326)
Reading layer `ageb_poligonos' from data source 
  `/Users/martin/Documents/EUI/RESEARCH/violence and climate/heat and DV mexico/data and regressions/output/ageb_poligonos.geojson' 
  using driver `GeoJSON'
Simple feature collection with 2431 features and 1 field
Geometry type: POLYGON
Dimension:     XY
Bounding box:  xmin: -99.34306 ymin: 19.12409 xmax: -98.94664 ymax: 19.59276
Geodetic CRS:  WGS 84
catalogo_ageb <- catalogo_ageb %>% 
  st_centroid() %>%
  mutate(lon = st_coordinates(.)[, 1],
         lat = st_coordinates(.)[, 2]) %>% 
  st_drop_geometry() %>% as_tibble()

I want to compute the distances between each station and the centroid of each AGEB.

# Compute the distances between each station and the col centroids
# create a df from all combinations of stations and municipalities
distances <- 
  crossing(
    (catalogo_ageb %>% select(ageb,
                             ageb_lon = lon,
                             ageb_lat = lat)),
    (catalogo_est %>% select(station,
                             sta_lon = longitud,
                             sta_lat = latitud))
  )

# compute the distance in meters, using haversine method, and convert to km
distances <- distances %>% 
  mutate(distance = geosphere::distHaversine(cbind(ageb_lon, ageb_lat),
                                             cbind(sta_lon, sta_lat)),
         distance = distance/1000)

distances %>% 
  write_rds(here("..", "output",
                 "temporary_data",
                 "weather",
                 "distances_pollution.rds"))

distances %>% 
  write_rds(here("..", "output",
                 "temporary_data",
                 "weather",
                 "distances_precipitation.rds"))

#Only keep the combinations that are within 20km
distances <- distances %>% 
  filter(distance <= 20)

3 Look at missing data in the pollution data

# encode missing variables!
hourly_data <- hourly_data %>% 
  mutate(across(where(is.numeric), ~ na_if(., -99)))

Vast majority of stations have data have at some point for some variables. So we keep them all.

hourly_data %>%
  group_by(station) %>%
  summarise(across(.cols = where(is.numeric) & !hora,
                   .fns = ~ sum(is.na(.)) / n())) %>%
  kable()
station co no no2 nox o3 pm10 pm25 pmco so2
ACO 0.4130948 0.4094128 0.4094246 0.4094010 0.4015059 0.3221417 1.0000000 1.0000000 0.4490653
AJM 0.3233218 0.3032949 0.3032949 0.3032949 0.3000260 0.3974462 0.3974698 0.3974462 0.2730245
AJU 1.0000000 1.0000000 0.9999882 0.9999646 0.2868674 1.0000000 0.4946186 1.0000000 1.0000000
ATI 0.2333483 0.3177988 0.3177988 0.3177988 0.1943566 0.2376204 1.0000000 1.0000000 0.2468726
BJU 0.1520723 1.0000000 0.4807874 1.0000000 0.1513878 0.1794869 0.1794987 0.1794751 0.2463888
CAM 0.2389657 0.2297371 0.2297371 0.2297371 0.2085182 0.2996011 0.2996011 0.2996011 0.2577181
CCA 0.1172111 0.1221795 0.1221795 0.1221795 0.0903158 1.0000000 0.1100477 1.0000000 0.1015743
CHO 0.3887132 0.6429617 0.6429617 0.6429617 0.3432189 0.3688633 1.0000000 1.0000000 0.4135786
COY 1.0000000 0.7613765 0.7613765 0.7613765 0.7438633 1.0000000 0.7371011 1.0000000 1.0000000
CUA 0.1442952 0.1732912 0.1732912 0.1732912 0.1455580 0.2439459 1.0000000 1.0000000 0.2458577
CUT 0.8409767 0.1309597 0.1309597 0.1309597 0.1178248 0.1363647 1.0000000 1.0000000 0.1229348
FAC 0.0992376 0.1048197 0.1048197 0.1048197 0.0928413 0.1936721 1.0000000 1.0000000 0.1192763
FAR 0.5744548 1.0000000 0.4904763 1.0000000 0.5180207 1.0000000 0.5103734 1.0000000 0.5065380
GAM 1.0000000 1.0000000 0.5002596 1.0000000 0.2157643 0.7056623 0.5309314 0.7056623 1.0000000
HGM 0.3827771 0.3836858 0.3836858 0.3836858 0.3779149 0.4898626 0.4898626 0.4898744 0.3722267
INN 0.3380617 1.0000000 1.0000000 1.0000000 0.2739568 0.4072059 0.4072059 0.4072295 0.2833860
IZT 0.2094505 0.2171804 0.2171804 0.2171804 0.1990889 0.2762816 1.0000000 1.0000000 0.2339029
LAA 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000 1.0000000
LLA 0.6706123 0.7467428 0.7467428 0.7467428 0.1929168 1.0000000 1.0000000 1.0000000 0.4798315
LPR 0.2030188 1.0000000 1.0000000 1.0000000 0.1764893 1.0000000 1.0000000 1.0000000 0.2182189
MER 0.1110154 0.1078762 0.1078762 0.1078762 0.0829754 0.1585395 0.1585395 0.1585395 0.0940096
MGH 0.0800014 0.1202559 0.1202559 0.1202559 0.0790219 0.5714690 0.5714690 0.5714690 0.1215422
MON 0.2363222 0.2044586 0.2044586 0.2044586 0.2164959 1.0000000 0.6349603 1.0000000 0.3303673
MPA 0.3881821 1.0000000 0.6100949 1.0000000 0.2928507 0.5930183 0.5930183 0.5930183 0.3097503
NEZ 0.1610059 0.1944038 0.1944038 0.1944038 0.1338864 0.7511447 0.2035380 1.0000000 0.1925864
PED 0.1498419 0.1429145 0.1429145 0.1429145 0.1301218 0.4462212 0.4462212 0.4462212 0.1384535
SAC 0.5287953 0.5089808 0.5089808 0.5089808 0.5169231 1.0000000 0.5448924 1.0000000 0.6960088
SAG 0.2102530 0.1710607 0.1710607 0.1710607 0.1600736 0.3222715 0.3222715 0.3222715 0.2844364
SFE 0.4136731 0.4002549 0.4002549 0.4002549 0.3572272 0.4343136 0.4343136 0.4343136 0.3565545
SJA 0.8049235 0.8322555 0.8320312 0.8320312 0.8065167 1.0000000 0.9337354 1.0000000 0.8034956
SUR 0.9803625 0.9803625 0.9803625 0.9803625 0.9663189 0.9520747 1.0000000 1.0000000 0.9846582
TAH 0.2696847 0.3297536 0.3297418 0.3297418 0.2386943 0.3592216 1.0000000 1.0000000 0.2691536
TLA 0.1502313 0.1426194 0.1426194 0.1426194 0.1195124 0.2131916 0.2131916 0.2131916 0.1217664
TLI 0.3835678 0.3681788 0.3681788 0.3681788 0.3733006 0.4794538 1.0000000 1.0000000 0.3769000
TPN 1.0000000 0.9829352 0.9829352 0.9829352 0.9517088 1.0000000 1.0000000 1.0000000 0.9829352
UAX 0.1469623 0.1341461 0.1341461 0.1341461 0.1146738 1.0000000 0.1874528 1.0000000 0.1471157
UIZ 0.1729253 0.1765601 0.1765601 0.1765601 0.1431151 0.3264020 0.3263902 0.3263902 0.1892112
VIF 0.1889634 0.1966461 0.1966461 0.1966461 0.1811863 0.2298315 1.0000000 1.0000000 0.1799825
XAL 0.4289676 0.4461740 0.4461740 0.4461740 0.4200812 0.5672796 0.5672796 0.5672796 0.4175911

We are going to look at daily averages. When doing so, it is crucial to know how many hours are missing for each day. For all variables, all 24 hours are recorded in +70% of the cases. Then, about 4% of the time, the station has some hours missing in the day.

hourly_missings <- hourly_data %>%
  group_by(fecha, station) %>%
  summarise(across(co:so2, ~ sum(is.na(.)), .names = "hours_missing_{.col}")) %>%
  mutate(across(starts_with("hours_missing"), ~ case_when(. == 0 ~ "0",
                                                          . %in% 1:4 ~ "1-4",
                                                          . %in% 5:23 ~ "5-23",
                                                          . == 24 ~ "24"),
                .names = "{.col}_fct")) %>% ungroup()

hourly_missings %>% count(hours_missing_pm25) %>% mutate(prop = n / sum(n)) %>% kable()
hours_missing_pm25 n prop
0 35436 0.2573252
1 6019 0.0437081
2 2278 0.0165421
3 1911 0.0138771
4 1513 0.0109869
5 855 0.0062087
6 475 0.0034493
7 394 0.0028611
8 284 0.0020623
9 220 0.0015976
10 188 0.0013652
11 149 0.0010820
12 157 0.0011401
13 146 0.0010602
14 145 0.0010529
15 114 0.0008278
16 95 0.0006899
17 70 0.0005083
18 87 0.0006318
19 52 0.0003776
20 26 0.0001888
21 39 0.0002832
22 25 0.0001815
23 122 0.0008859
24 86909 0.6311062 
hourly_missings %>% count(hours_missing_pm25_fct) %>% mutate(prop = n / sum(n)) %>% kable()
hours_missing_pm25_fct n prop
0 35436 0.2573252
1-4 11721 0.0851143
24 86909 0.6311062
5-23 3643 0.0264543 
hourly_missings %>% count(hours_missing_pm10_fct) %>% mutate(prop = n / sum(n)) %>% kable()
hours_missing_pm10_fct n prop
0 41620 0.3022315
1-4 14505 0.1053308
24 77060 0.5595858
5-23 4524 0.0328519 
hourly_missings %>% count(hours_missing_so2_fct) %>% mutate(prop = n / sum(n)) %>% kable()
hours_missing_so2_fct n prop
0 58554 0.4252010
1-4 24460 0.1776209
24 51079 0.3709198
5-23 3616 0.0262583

We can plot this station recording activity:

hourly_missings %>% 
  ggplot(aes(x = fecha, y = station, col = hours_missing_pm25_fct)) +
  geom_point(shape = 15) +
  scale_color_manual(values = c("0" = "green",
                                "1-4" = "blue",
                                "5-23" = "red",
                                "24" = "gray")) +
  labs(title = "Station activity for pm25") +
  theme(legend.position = "bottom")

ggsave(here("..", "output",
            "figures",
            "stations_activity_pollution.png"),
       width = 10,
       height = 10,
       dpi = 300)

4 Matching procedure

Now, I want to go from station level to AGEB level. This is quite heavy because I want to work at the hourly level.

I compute a distance weighted average of all variables for eahc AGEB and each hour of each day. I then compute daily averages for every AGEB.

I have 3143 days and 24 hourly measures for each. I have to do it day by day, to not exhaust memory. I assign to each AGEB per day per hour the measures of all stations within 20km. I then compute a distance weighted avergae for each hour. Finally, I compute daily averages to end up at the day per AGEB level.

# this long function computes daily averages at the AGEB level
# from hourly data at the station level
# It also takes care of missing values

process_data_for_one_day <- function(fecha) {
  
  hourly_data_one_day <- hourly_data %>% 
    filter(fecha == !!fecha)
  
  one_day_hourly <- 
    # take each AGEB and station combination that are within 20km of each other
    distances %>% select(ageb, station, distance) %>% 
    # add date to each observation
    mutate(fecha = hourly_data_one_day %>% pull(fecha) %>% unique(), .before = 1) %>% 
    # multiply each obs by number of hours
    expand_grid(hora = 1:24) %>% relocate(hora, .after = "fecha") %>% 
    # Add the actual data (day x station x hour) to each AGEB
    left_join(hourly_data_one_day,
              by = c("station", "fecha", "hora")) %>% 
    # Now, i want to move to the AGEB by date by hour level
    # I take inverse distance weighted averages, so I compute a weight based on distance
    mutate(weight = 1/distance) %>% 
    group_by(ageb, fecha, hora) %>% 
    # compute idw averages
    summarise(across(.cols = co:so2, 
                     .fns = ~ weighted.mean(., weight, na.rm = T)),
              .groups = "drop")
  
  one_day <- one_day_hourly %>% 
    # Summarise hourly data to daily observations
    #I reduce each day to a single observation, by applying different functions.
    group_by(ageb, fecha) %>%
    summarise(
      #i want to count number of missing hours per day x AGEB
      across(co:so2, ~ sum(is.na(.)), .names = "nr_hrs_NA_{.col}"),
      # If all hours are recorded, i take mean max min...
      # if 1-4 are missing, i still take an average, max, min
      # if more than 4 hours are missing (or all), I want to set the value to NA
      pm25_hourly = list(pm25),
      pm10_hourly = list(pm10),
      
      co = 
        if_else(nr_hrs_NA_co > 4, NA, mean(co, na.rm = T)),
      no =
        if_else(nr_hrs_NA_no > 4, NA, mean(no, na.rm = T)),
      no2 =
        if_else(nr_hrs_NA_no2 > 4, NA, mean(no2, na.rm = T)),
      nox =
        if_else(nr_hrs_NA_nox > 4, NA, mean(nox, na.rm = T)),
      o3 =
        if_else(nr_hrs_NA_o3 > 4, NA, mean(o3, na.rm = T)),
      pmco =
        if_else(nr_hrs_NA_pmco > 4, NA, mean(pmco, na.rm = T)),
      so2 =
        if_else(nr_hrs_NA_so2 > 4, NA, mean(so2, na.rm = T)),
      
      .groups = "drop"
    ) %>% 
    select(-nr_hrs_NA_co, -nr_hrs_NA_no, -nr_hrs_NA_no2, -nr_hrs_NA_nox,
           -nr_hrs_NA_o3, -nr_hrs_NA_pmco, -nr_hrs_NA_so2)
  
  return(one_day)
}

# just as an example for one day.
# I have one observation per AGEB.
# process_data_for_one_day(fechas[1])
# process_data_for_one_day(fechas[3])

5 Run the procedure on each day, bind and save data

# To test the code on a small subset
# hourly_data <- hourly_data %>%
#    filter(fecha %in% seq(as.Date("2019-01-01"), as.Date("2019-01-31"), "days"))

# get all the different dates
fechas <- hourly_data %>% 
  pull(fecha) %>% 
  unique()

# set parallel computing
parallel::detectCores()
[1] 10
plan(multisession, workers = 6)
# set memory limit
mem.maxVSize(vsize = 40000)
[1] 40000
# run for each day
tic()
list_of_days <-
  # use map insetad of future_map to run single core with purrr
  future_map(fechas, ~ process_data_for_one_day(.x),
      .progress = TRUE)
toc() 
7374.396 sec elapsed
# to go back to single core
plan(sequential)

#Bind the dataframes
pollution_daily <- 
  list_rbind(list_of_days) 

# Save the data
pollution_daily %>% 
  write_rds(here("..", "output",
                 "temporary_data",
                 "weather",
                 "processed_rama_20.rds"))