# Environmental Data Analysis

    library(mongolstats)
    library(dplyr)
    library(ggplot2)
    library(tidyr)
    library(stringr)

    nso_options(mongolstats.lang = "en")

    # Global theme with proper margins to prevent text cutoff
    theme_set(
      theme_minimal(base_size = 11) +
        theme(
          plot.margin = margin(10, 10, 10, 10),
          plot.title = element_text(size = 13, face = "bold"),
          plot.subtitle = element_text(size = 10, color = "grey40"),
          legend.text = element_text(size = 9),
          legend.title = element_text(size = 10)
        )
    )

## Overview

Air quality is a primary environmental determinant of health in Mongolia. The NSO’s network of monitoring stations provides high-resolution data essential for exposure assessment. Unlike aggregated regional data, these station-level measurements allow for localized exposure assessment—critical for environmental health research.

This guide demonstrates how to: - Analyze air quality trends across monitoring stations - Compare pollutant levels against regulatory standards - Map water quality across river basins - Track climate-related health risks (dust exposure)

## Air Quality Monitoring

### Pollutant Concentrations by Station

The NSO maintains air quality monitoring stations that measure key pollutants. We’ll focus on the Top 10 most polluted stations in Ulaanbaatar to understand the severity of exposure.

> Important: Air Quality Table Structures

> The NSO provides two types of air quality tables with different dimension structures:

> - Annual table (`DT_NSO_2024_135V01`): Uses a `Year` dimension for filtering (e.g., `selections = list(Year = "2024")`)
> - Monthly tables (`DT_NSO_2400_015V1` through `V6`): Use a running `Month` dimension with codes `0`, `1`, `2`, etc. (where `0` = most recent month). Filter by month codes, not year labels: `selections = list(Month = as.character(0:47))` retrieves the last 48 months.

> The monthly tables are split by pollutant: V1 (SO₂), V2 (NO₂), V3 (CO), V4 (PM10), V5 (PM2.5), V6 (O₃). The `Month_en` column returns dates in `"YYYY-MM"` format.

    # 1. Identify Top 10 Polluted Stations in UB (using 2024 annual data)
    air_annual <- tryCatch(
      {
        nso_data(
          "DT_NSO_2024_135V01",
          selections = list(Year = "2024"),
          labels = "en"
        )
      },
      error = function(e) NULL
    )

    if (is.null(air_annual) || nrow(air_annual) == 0) {
      air_annual <- utils::read.csv(
        system.file("extdata", "air_annual_cached.csv", package = "mongolstats"),
        check.names = FALSE,
        stringsAsFactors = FALSE
      )
    }

    air_annual <- air_annual |>
      filter(!is.na(value)) |>
      mutate(
        Station = str_trim(`Station location_en`),
        Pollutant = str_trim(Indicator_en)
      )

    # Define UB Stations
    ub_stations <- c(
      "Misheel-Expo center", "West crossroad", "1st micro district",
      "13th micro district", "32nd Toirog", "Ofitseruudiin ordon",
      "Kharkhorin market", "Urgakh naran micro district", "Dambdarjaa",
      "Khailaast", "Tolgoit", "Zuragt", "Amgalan", "Nisekh",
      "Tavan buudal", "Bayankhoshuu", "Sharkhad", "100 ail"
    )

    # Rank UB stations by PM2.5 levels
    top_10_stations <- air_annual |>
      filter(Station %in% ub_stations) |>
      filter(str_detect(Pollutant, "PM2.5")) |>
      arrange(desc(value)) |>
      head(10) |>
      pull(Station)

    # Default to key UB stations if ranking fails to ensure facets are not empty
    if (length(top_10_stations) == 0) {
      top_10_stations <- c(
        "Bayankhoshuu", "Tolgoit", "Zuragt", "Khailaast", "Amgalan",
        "Misheel-Expo center", "West crossroad", "1st micro district", "100 ail", "Nisekh"
      )
    }

    # 2. Fetch Monthly Data for Detailed Trends
    # Monthly tables are pollutant-specific: V1=SO2, V2=NO2, V5=PM2.5.
    # We fetch all three to ensure trends and compliance plots work.
    air_monthly <- tryCatch(
      {
        nso_package(
          list(
            list(tbl_id = "DT_NSO_2400_015V1", selections = list(Month = as.character(0:47))), # SO2
            list(tbl_id = "DT_NSO_2400_015V2", selections = list(Month = as.character(0:47))), # NO2
            list(tbl_id = "DT_NSO_2400_015V3", selections = list(Month = as.character(0:47))), # CO
            list(tbl_id = "DT_NSO_2400_015V4", selections = list(Month = as.character(0:47))), # PM10
            list(tbl_id = "DT_NSO_2400_015V5", selections = list(Month = as.character(0:47)))  # PM2.5
          ),
          labels = "en"
        )
      },
      error = function(e) NULL
    )

    # Robust fallback: use cache if API fails, returns no rows, or lacks PM2.5
    if (is.null(air_monthly) || nrow(air_monthly) == 0) {
      air_monthly <- utils::read.csv(
        system.file("extdata", "air_monthly_cached.csv", package = "mongolstats"),
        check.names = FALSE,
        stringsAsFactors = FALSE
      )
    }

    air_monthly <- air_monthly |>
      filter(!is.na(value)) |>
      # Standardize indicator column name which can vary between live fetch and cache
      rename_with(~ "Pollutant_Raw", any_of(c("Indicator_en", "Indicator of air pollution_en"))) |>
      mutate(
        Station = str_trim(Location_en),
        Pollutant_Raw = str_trim(Pollutant_Raw),
        # If Month_en is already YYYY-MM, Date will work, else handle gracefully
        Date = as.Date(paste0(Month_en, "-01"))
      ) |>
      # Focus on recent years for trends
      filter(Date >= as.Date("2021-01-01"), Date <= as.Date("2025-12-31")) |>
      filter(Station %in% top_10_stations)

    # Clean and Reshape Data for Ribbons
    air_trends <- air_monthly |>
      mutate(
        Pollutant_Type = case_when(
          str_detect(Pollutant_Raw, "PM2.5") ~ "PM2.5",
          str_detect(Pollutant_Raw, "Sulphur dioxide|SO2") ~ "SO2",
          str_detect(Pollutant_Raw, "Nitrogen dioxide|NO2") ~ "NO2",
          TRUE ~ NA_character_
        ),
        Measure = case_when(
          str_detect(Pollutant_Raw, "Average") ~ "Average",
          str_detect(Pollutant_Raw, "Maximum") ~ "Maximum",
          str_detect(Pollutant_Raw, "Minimum") ~ "Minimum",
          TRUE ~ "Other"
        )
      ) |>
      filter(!is.na(Pollutant_Type), Measure %in% c("Average", "Maximum")) |>
      # Use Average for the trend line and Maximum for the upper ribbon bound
      # Outlier Removal: Group by Station and Pollutant to detect anomalies
      # Using IQR method (3 * IQR) to remove extreme outliers while keeping natural spikes
      group_by(Station, Pollutant_Type, Measure) |>
      mutate(
        Q1 = quantile(value, 0.25, na.rm = TRUE),
        Q3 = quantile(value, 0.75, na.rm = TRUE),
        IQR = Q3 - Q1,
        Lower = Q1 - 3 * IQR,
        Upper = Q3 + 3 * IQR,
        Is_Outlier = value < Lower | value > Upper
      ) |>
      filter(!Is_Outlier) |>
      ungroup() |>
      select(Station, Date, Pollutant_Type, Measure, value) |>
      pivot_wider(names_from = Measure, values_from = value)

    # Note: If "Minimum" isn't explicitly available, we'll just show Average and Max range

### PM2.5 Trends: High-Risk Stations

Fine Particulate Matter (PM2.5) poses the greatest health risk among air pollutants due to its ability to penetrate deep into the lungs and bloodstream. The following trends reveal chronic winter pollution crisis across Ulaanbaatar’s most exposed neighborhoods.

What the dashed line means: The horizontal dashed red line represents Mongolia’s Maximum Allowable Concentration (MAC) for PM2.5 annual average (0.025 mg/m³). Values above this line indicate non-compliance with national air quality standards and heightened health risks.

    # MAC annual average for PM2.5
    mac_pm25 <- 0.025

    p_pm <- air_trends |>
      filter(Pollutant_Type == "PM2.5") |>
      ggplot(aes(x = Date, group = Station)) +
      # MAC reference line - regulatory threshold for health protection
      geom_hline(yintercept = mac_pm25, linetype = "dashed", color = "darkred", linewidth = 1) +
      # Ribbon shows range from average to maximum - captures daily variability
      geom_ribbon(aes(ymin = Average, ymax = Maximum), fill = "red", alpha = 0.2) +
      geom_line(aes(y = Average), color = "firebrick", linewidth = 0.8) +
      facet_wrap(~Station, ncol = 2, scales = "free_y") +  # each station on its own scale
      scale_x_date(date_breaks = "6 months", date_labels = "%b %Y") +
      labs(
        title = "PM2.5 Trends in Top 10 Polluted UB Stations (2021-2025)",
        subtitle = "Monthly Average (Line), Maximum (Ribbon), and MAC Limit (Dashed Line)",
        x = NULL,
        y = "Concentration (mg/m³)"
      ) +
      scale_y_continuous(limits = c(0, NA)) +  # y-axis starts at 0 for honest comparison
      theme_minimal(base_size = 12) +
      theme(
        plot.title = element_text(face = "bold"),
        strip.text = element_text(face = "bold"),
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)
      )

    p_pm  # print static ggplot

> Key Findings: All 10 stations show systematic winter exceedances, with peak concentrations reaching 4-10x the MAC limit during heating season (November-February). Even during summer months, many stations hover near or above the regulatory threshold, indicating year-round exposure with dangerous seasonal spikes.

### Sulfur Dioxide (SO₂) Trends: The Coal Combustion Signature

Sulfur dioxide is a direct tracer of raw coal combustion for residential heating. Unlike PM2.5 (which has multiple sources), SO₂ patterns directly reflect fuel use intensity, making it a critical indicator for evaluating clean energy transition policies.

What the dashed line means: The horizontal dashed orange line represents Mongolia’s MAC for SO₂ annual average (0.020 mg/m³). Exceedances indicate both regulatory non-compliance and respiratory health risks, particularly for vulnerable populations (children, elderly, asthmatics).

    # MAC annual average for SO2
    mac_so2 <- 0.020

    p_so2 <- air_trends |>
      filter(Pollutant_Type == "SO2") |>
      ggplot(aes(x = Date, group = Station)) +
      # MAC reference line - SO2 threshold for respiratory health
      geom_hline(yintercept = mac_so2, linetype = "dashed", color = "darkorange3", linewidth = 1) +
      geom_ribbon(aes(ymin = Average, ymax = Maximum), fill = "orange", alpha = 0.2) +
      geom_line(aes(y = Average), color = "darkorange", linewidth = 0.8) +
      facet_wrap(~Station, ncol = 2, scales = "free_y") +
      scale_x_date(date_breaks = "6 months", date_labels = "%b %Y") +
      labs(
        title = "SO₂ Trends in Top 10 Polluted UB Stations (2021-2025)",
        subtitle = "Monthly Average (Line), Maximum (Ribbon), and MAC Limit (Dashed Line)",
        x = NULL,
        y = "Concentration (mg/m³)"
      ) +
      theme_minimal(base_size = 12) +
      theme(
        plot.title = element_text(face = "bold"),
        strip.text = element_text(face = "bold"),
        panel.grid.minor = element_blank(),
        axis.text.x = element_text(angle = 45, hjust = 1)
      )

    p_so2  # print static ggplot

> Critical Pattern: SO₂ shows extreme seasonal variation (20-30x increase in winter vs. summer), far exceeding PM2.5’s seasonal swing. This confirms that residential coal heating is the dominant driver of winter air pollution. Peak concentrations during January-February can reach 10-30x the MAC limit, representing acute health hazard conditions.

### Regulatory Compliance: Exceedance Factor

The Exceedance Factor quantifies how many times a station’s annual average exceeds the regulatory limit. A factor of 1.0 represents the threshold; values >1.0 indicate non-compliance.

To clearly see how much stations exceed safety limits, we calculate the Exceedance Factor (Ratio of Annual Average to MAC). A value > 1.0 indicates non-compliance.

    # 1. Get MAC Standards
    mac_so2 <- 0.020 # Annual average mg/m3
    mac_no2 <- 0.030 # Annual average mg/m3
    mac_pm25 <- 0.025 # Annual average mg/m3 (WHO/National target approximation)

    # 2. Calculate Exceedance for 2024
    compliance <- air_annual |>
      filter(Station %in% top_10_stations) |>
      mutate(
        Pollutant_Type = case_when(
          str_detect(Pollutant, "PM2.5") ~ "PM2.5",
          str_detect(Pollutant, "Sulphur dioxide") ~ "SO2",
          str_detect(Pollutant, "Nitrogen dioxide") ~ "NO2",
          TRUE ~ NA_character_
        ),
        MAC = case_when(
          Pollutant_Type == "PM2.5" ~ mac_pm25,
          Pollutant_Type == "SO2" ~ mac_so2,
          Pollutant_Type == "NO2" ~ mac_no2,
          TRUE ~ NA_real_
        )
      ) |>
      filter(!is.na(Pollutant_Type)) |>
      mutate(
        Exceedance_Factor = value / MAC,
        Status = ifelse(Exceedance_Factor > 1, "Non-Compliant", "Compliant")
      )

    # 3. Plot regulatory compliance - exceedance factor shows how many times over the limit
    p_comp <- compliance |>
      ggplot(aes(x = reorder(Station, Exceedance_Factor), y = Exceedance_Factor, fill = Status)) +
      geom_col() +
      geom_hline(yintercept = 1, linetype = "dashed", color = "black", linewidth = 1) +  # compliance threshold
      geom_text(aes(label = round(Exceedance_Factor, 1)), hjust = -0.1, size = 2.5) +  # inline labels
      facet_wrap(~Pollutant_Type, scales = "free_x") +
      coord_flip() +
      scale_fill_manual(values = c("Compliant" = "#27ae60", "Non-Compliant" = "#c0392b")) +
      scale_y_continuous(expand = expansion(mult = c(0, 0.15))) +
      labs(
        title = "Regulatory Compliance: Exceedance Factors (2024)",
        subtitle = "Ratio of Annual Average to Maximum Allowable Concentration (MAC)",
        y = "Times above Safety Limit",
        x = NULL
      ) +
      theme_minimal(base_size = 12) +
      theme(
        plot.title = element_text(face = "bold"),
        legend.position = "bottom",
        panel.grid.major.y = element_blank()
      )

    p_comp  # print static ggplot

> Compliance Crisis: 90% of top stations are non-compliant for PM2.5, with exceedance factors reaching 2.0-2.5x (equivalent to annual averages of 0.050-0.063 mg/m³). Only the 13th micro district achieves compliance for PM2.5, suggesting localized factors (wind patterns, lower housing density, or cleaner fuel access) warrant investigation for replication elsewhere.

> SO₂ non-compliance is universal across all 10 stations, with factors of 2-4x, underscoring the pervasive reliance on raw coal for heating.

## Water Quality Monitoring

Electrical conductivity serves as a proxy for total dissolved solids and water mineralization. Values >1,000 µS/cm typically indicate significant mineral content; values >10,000 µS/cm suggest either natural saline conditions (endorheic basins) or severe anthropogenic pollution.

Water quality is monitored at specific river and lake stations across Mongolia’s major basins. We focus on the Top 15 stations with the highest electrical conductivity.

    # Fetch water quality data
    water <- nso_data(
      "DT_NSO_2300_005V12",
      selections = list(),
      labels = "en"
    ) |>
      filter(!is.na(value)) |>
      mutate(
        Station = str_trim(`Station location_en`),
        Indicator = str_trim(Indicator_en)
      )

    # Focus on electrical conductivity & Top 15
    conductivity <- water |>
      filter(str_detect(Indicator, "Electrical conductivity")) |>
      slice_max(order_by = value, n = 15)

    # Plot water quality - top stations by conductivity
    p <- conductivity |>
      mutate(Station = reorder(Station, value)) |>
      ggplot(aes(x = Station, y = value, fill = value)) +
      geom_col(show.legend = FALSE) +
      geom_text(aes(label = scales::comma(value)), hjust = -0.1, size = 3) +  # inline labels
      coord_flip() +
      scale_y_continuous(labels = scales::comma, expand = expansion(mult = c(0, 0.15))) +
      scale_fill_viridis_c(option = "viridis") +
      labs(
        title = "Top 15 Water Stations by Conductivity (2023)",
        subtitle = "Higher values may indicate increased mineralization or pollution",
        x = NULL,
        y = "Conductivity (μS/cm)"
      ) +
      theme_minimal(base_size = 12) +
      theme(
        plot.title = element_text(face = "bold"),
        panel.grid.major.y = element_blank(),
        panel.grid.minor = element_blank()
      )

    p  # print static ggplot

> Interpreting Conductivity: A Tale of Three Sources

> The top 15 stations reveal three distinct environmental stories driven by geology, industry, and climate:

> 1. Natural Saline Basins (The Extremes > 10,000 µS/cm): The highest values belong to naturally hypersaline, endorheic lakes.
>    - Uvs/Davst & Gashuun: Uvs Lake, a UNESCO site and Mongolia’s largest saline lake.
>    - Khyargas: A major salt lake in the Great Lakes Depression.
>    - Sangiin dalai: A saline lake in Ulziit sum, Uvurkhangai province, distinct from the larger northern lake of the same name.
>    - Taigam/Delger: Likely a saline wetland or spring in the Govi-Altai mineral belt.
> 2. Industrial & Urban Pollution (The “Hidden” Highs): Elevated conductivity in freshwater rivers often signals contamination.
>    - Khangal (Erdenet): The Khangal River is heavily impacted by mining activities and wastewater from Erdenet city, showing mineralization 2x higher than historical baselines.
>    - Tuul (Songino) & Dund gol: These stations capture the urban wastewater footprint of Ulaanbaatar, where treated and untreated effluent raises ion concentrations downstream.
> 3. Climate-Sensitive Lakes (The “Shrinking” Signals):
>    - Ganga, Duut, & Ugii: These traditionally freshwater/brackish lakes are experiencing secondary salinization. As water levels drop due to climate change and evaporation, mineral concentrations rise, turning them into sensitive indicators of regional aridification.

> Conclusion: While Uvs and Khyargas are naturally “salty,” the elevated readings in the Khangal and Tuul rivers are actionable signals of anthropogenic pressure.

## Climate Exposure: Dust Days

Dust storms pose respiratory health risks through coarse particulate (PM10) inhalation and serve as a climate change indicator. Desertification and reduced soil moisture amplify dust mobilization in Mongolia’s Gobi region.

Dust storms are a significant environmental health concern. Here are the Top 10 stations with the highest number of dust days in 2024.

    # Fetch dust day data
    dust <- nso_data(
      "DT_NSO_2400_028V1",
      selections = list(),
      labels = "en"
    ) |>
      filter(!is.na(value)) |>
      mutate(
        Station = str_trim(`Station location_en`),
        Month_date = Month_en
      )

    # Parse dates and aggregate to annual
    dust_annual <- dust |>
      mutate(
        Year = as.numeric(str_sub(Month_date, 1, 4))
      ) |>
      filter(Year == 2024) |>
      group_by(Station) |>
      summarize(
        Total_dust_days = sum(value, na.rm = TRUE),
        .groups = "drop"
      ) |>
      slice_max(Total_dust_days, n = 10)

    # Plot dust exposure by station
    p <- dust_annual |>
      mutate(Station = reorder(Station, Total_dust_days)) |>
      ggplot(aes(x = Station, y = Total_dust_days, fill = Total_dust_days)) +
      geom_col(show.legend = FALSE) +
      geom_text(aes(label = Total_dust_days), hjust = -0.2, size = 3.5) +  # inline labels
      coord_flip() +
      scale_fill_viridis_c(option = "magma", direction = -1) +
      scale_y_continuous(expand = expansion(mult = c(0, 0.15))) +
      labs(
        title = "Top 10 Stations for Dust Exposure (2024)",
        subtitle = "Total annual dust days",
        x = NULL,
        y = "Days",
        caption = "Source: NSO Mongolia"
      ) +
      theme_minimal(base_size = 12) +
      theme(
        plot.title = element_text(face = "bold"),
        panel.grid.major.y = element_blank()
      )

    p  # print static ggplot

> Gobi Dust Belt: Sainshand (30 days/year) and Dalanzadgad (21 days/year) represent the core of Mongolia’s dust exposure zone. Chronic dust exposure at this frequency (equivalent to ~6-8% of the year) contributes to elevated respiratory disease burden. Climate projections suggest increasing dust frequency due to reduced precipitation and vegetation loss, particularly in the Gobi-steppe ecotone.

## Geographic Context

For spatial analysis, station-level environmental data ideally should be joined with geographic coordinates. While the NSO data provides station names, a separate lookup table with lat/lon coordinates would enable:

- Spatial interpolation of pollutant concentrations
- Distance-based exposure assignment for health studies
- Hot spot identification using spatial statistics

Example workflow:

    # Hypothetical station metadata table (user would compile this)
    station_metadata <- tribble(
      ~Station_name, ~Latitude, ~Longitude, ~Aimag,
      "Ulaanbaatar", 47.9221, 106.9155, "Ulaanbaatar",
      "Darkhan", 49.4869, 105.9228, "Darkhan-Uul",
      "Erdenet", 49.0333, 104.0833, "Orkhon"
      # ... add all stations
    )

    # Join with environmental data
    air_with_location <- air_quality |>
      left_join(station_metadata, by = c("Station" = "Station_name"))

    # Now you can create maps, calculate distances, etc.

## Next Steps

- Discover More Tables: See Discovery Guide for search techniques
- Mapping: Learn spatial analysis in Mapping Guide
- Reference: Browse all functions in the Reference

## Key Environmental Tables